Overview

Dataset statistics

Number of variables57
Number of observations232130
Missing cells45628
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory100.9 MiB
Average record size in memory456.0 B

Variable types

Numeric31
Categorical26

Warnings

날짜 has a high cardinality: 219007 distinct values High cardinality
유저이름 has a high cardinality: 28397 distinct values High cardinality
선수1 has a high cardinality: 4632 distinct values High cardinality
선수2 has a high cardinality: 4680 distinct values High cardinality
선수3 has a high cardinality: 4897 distinct values High cardinality
선수4 has a high cardinality: 4998 distinct values High cardinality
선수5 has a high cardinality: 5137 distinct values High cardinality
선수6 has a high cardinality: 5265 distinct values High cardinality
선수7 has a high cardinality: 5413 distinct values High cardinality
선수8 has a high cardinality: 5413 distinct values High cardinality
선수9 has a high cardinality: 5626 distinct values High cardinality
선수10 has a high cardinality: 5680 distinct values High cardinality
선수11 has a high cardinality: 5702 distinct values High cardinality
선수12 has a high cardinality: 5859 distinct values High cardinality
선수13 has a high cardinality: 5956 distinct values High cardinality
선수14 has a high cardinality: 6080 distinct values High cardinality
선수15 has a high cardinality: 6141 distinct values High cardinality
선수16 has a high cardinality: 6221 distinct values High cardinality
선수17 has a high cardinality: 6462 distinct values High cardinality
선수18 has a high cardinality: 6773 distinct values High cardinality
전체슈팅 is highly correlated with 유효슈팅High correlation
유효슈팅 is highly correlated with 전체슈팅High correlation
드리블횟수 is highly correlated with 패스시도 and 1 other fieldsHigh correlation
패스시도 is highly correlated with 드리블횟수 and 3 other fieldsHigh correlation
패스성공 is highly correlated with 드리블횟수 and 3 other fieldsHigh correlation
숏패스시도 is highly correlated with 패스시도 and 2 other fieldsHigh correlation
숏패스성공 is highly correlated with 패스시도 and 2 other fieldsHigh correlation
쓰루패스시도 is highly correlated with 쓰루패스성공High correlation
쓰루패스성공 is highly correlated with 쓰루패스시도High correlation
드리븐패스시도 is highly correlated with 드리븐패스성공High correlation
드리븐패스성공 is highly correlated with 드리븐패스시도High correlation
선수1 has 2466 (1.1%) missing values Missing
선수2 has 2493 (1.1%) missing values Missing
선수3 has 2498 (1.1%) missing values Missing
선수4 has 2557 (1.1%) missing values Missing
선수5 has 2595 (1.1%) missing values Missing
선수6 has 2523 (1.1%) missing values Missing
선수7 has 2520 (1.1%) missing values Missing
선수8 has 2504 (1.1%) missing values Missing
선수9 has 2484 (1.1%) missing values Missing
선수10 has 2477 (1.1%) missing values Missing
선수11 has 2564 (1.1%) missing values Missing
선수12 has 2549 (1.1%) missing values Missing
선수13 has 2505 (1.1%) missing values Missing
선수14 has 2507 (1.1%) missing values Missing
선수15 has 2578 (1.1%) missing values Missing
선수16 has 2552 (1.1%) missing values Missing
선수17 has 2575 (1.1%) missing values Missing
선수18 has 2679 (1.2%) missing values Missing
matchID is uniformly distributed Uniform
날짜 is uniformly distributed Uniform
matchID has unique values Unique
득점수 has 55548 (23.9%) zeros Zeros
전체슈팅 has 6963 (3.0%) zeros Zeros
유효슈팅 has 11231 (4.8%) zeros Zeros
자살골 has 227623 (98.1%) zeros Zeros
헤딩슛 has 139847 (60.2%) zeros Zeros
헤딩골 has 207160 (89.2%) zeros Zeros
프리킥 has 208477 (89.8%) zeros Zeros
중거리슛 has 67168 (28.9%) zeros Zeros
중거리골 has 180282 (77.7%) zeros Zeros
파울 has 123275 (53.1%) zeros Zeros
옐로카드 has 219603 (94.6%) zeros Zeros
드리블횟수 has 2496 (1.1%) zeros Zeros
코너킥횟수 has 75361 (32.5%) zeros Zeros
점유율 has 2346 (1.0%) zeros Zeros
오프사이드횟수 has 164372 (70.8%) zeros Zeros
평점 has 2705 (1.2%) zeros Zeros
블락시도 has 4290 (1.8%) zeros Zeros
블락성공 has 119967 (51.7%) zeros Zeros
태클시도 has 3050 (1.3%) zeros Zeros
태클성공 has 4107 (1.8%) zeros Zeros
패스시도 has 2487 (1.1%) zeros Zeros
패스성공 has 2523 (1.1%) zeros Zeros
숏패스시도 has 2807 (1.2%) zeros Zeros
숏패스성공 has 2816 (1.2%) zeros Zeros
롱패스시도 has 11239 (4.8%) zeros Zeros
롱패스성공 has 24078 (10.4%) zeros Zeros
쓰루패스시도 has 3072 (1.3%) zeros Zeros
쓰루패스성공 has 3181 (1.4%) zeros Zeros
드리븐패스시도 has 46754 (20.1%) zeros Zeros
드리븐패스성공 has 58402 (25.2%) zeros Zeros

Reproduction

Analysis started2021-01-27 09:07:00.161870
Analysis finished2021-01-27 09:10:37.091502
Duration3 minutes and 36.93 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

matchID
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct232130
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1668367.5
Minimum1552303
Maximum1784432
Zeros0
Zeros (%)0.0%
Memory size1.8 MiB
2021-01-27T18:10:37.229330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1552303
5-th percentile1563909.45
Q11610335.25
median1668367.5
Q31726399.75
95-th percentile1772825.55
Maximum1784432
Range232129
Interquartile range (IQR)116064.5

Descriptive statistics

Standard deviation67010.30333
Coefficient of variation (CV)0.04016519342
Kurtosis-1.2
Mean1668367.5
Median Absolute Deviation (MAD)58032.5
Skewness-7.916683931 × 1016
Sum3.872781478 × 1011
Variance4490380752
MonotocityStrictly increasing
2021-01-27T18:10:37.341403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15749111
 
< 0.1%
16899641
 
< 0.1%
15589561
 
< 0.1%
15691951
 
< 0.1%
15712421
 
< 0.1%
15650971
 
< 0.1%
15671441
 
< 0.1%
15528031
 
< 0.1%
15548501
 
< 0.1%
16592631
 
< 0.1%
Other values (232120)232120
> 99.9%
ValueCountFrequency (%)
15523031
< 0.1%
15523041
< 0.1%
15523051
< 0.1%
15523061
< 0.1%
15523071
< 0.1%
ValueCountFrequency (%)
17844321
< 0.1%
17844311
< 0.1%
17844301
< 0.1%
17844291
< 0.1%
17844281
< 0.1%

득점수
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.490647482
Minimum0
Maximum22
Zeros55548
Zeros (%)23.9%
Memory size1.8 MiB
2021-01-27T18:10:37.439561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum22
Range22
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.275694979
Coefficient of variation (CV)0.8557992376
Kurtosis5.168281408
Mean1.490647482
Median Absolute Deviation (MAD)1
Skewness1.264076846
Sum346024
Variance1.627397678
MonotocityNot monotonic
2021-01-27T18:10:37.519204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
173871
31.8%
257998
25.0%
055548
23.9%
330074
13.0%
410443
 
4.5%
52802
 
1.2%
6739
 
0.3%
7253
 
0.1%
8144
 
0.1%
997
 
< 0.1%
Other values (11)161
 
0.1%
ValueCountFrequency (%)
055548
23.9%
173871
31.8%
257998
25.0%
330074
13.0%
410443
 
4.5%
ValueCountFrequency (%)
221
 
< 0.1%
211
 
< 0.1%
191
 
< 0.1%
174
< 0.1%
168
< 0.1%

전체슈팅
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.997583251
Minimum0
Maximum30
Zeros6963
Zeros (%)3.0%
Memory size1.8 MiB
2021-01-27T18:10:37.605788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile10
Maximum30
Range30
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.757730974
Coefficient of variation (CV)0.5518129135
Kurtosis1.348317045
Mean4.997583251
Median Absolute Deviation (MAD)2
Skewness0.7387509059
Sum1160089
Variance7.605080124
MonotocityNot monotonic
2021-01-27T18:10:37.696689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
435650
15.4%
533829
14.6%
331488
13.6%
628819
12.4%
222382
9.6%
721768
9.4%
814989
6.5%
111979
 
5.2%
99963
 
4.3%
06963
 
3.0%
Other values (21)14300
6.2%
ValueCountFrequency (%)
06963
 
3.0%
111979
 
5.2%
222382
9.6%
331488
13.6%
435650
15.4%
ValueCountFrequency (%)
301
 
< 0.1%
293
< 0.1%
282
 
< 0.1%
272
 
< 0.1%
266
< 0.1%

유효슈팅
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.821242407
Minimum0
Maximum28
Zeros11231
Zeros (%)4.8%
Memory size1.8 MiB
2021-01-27T18:10:37.790697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile8
Maximum28
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.268887564
Coefficient of variation (CV)0.5937565123
Kurtosis1.589856545
Mean3.821242407
Median Absolute Deviation (MAD)2
Skewness0.7849445089
Sum887025
Variance5.14785078
MonotocityNot monotonic
2021-01-27T18:10:37.877545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
342758
18.4%
440013
17.2%
236188
15.6%
531424
13.5%
122142
9.5%
621142
9.1%
712853
 
5.5%
011231
 
4.8%
87065
 
3.0%
93649
 
1.6%
Other values (18)3665
 
1.6%
ValueCountFrequency (%)
011231
 
4.8%
122142
9.5%
236188
15.6%
342758
18.4%
440013
17.2%
ValueCountFrequency (%)
281
 
< 0.1%
261
 
< 0.1%
254
< 0.1%
241
 
< 0.1%
234
< 0.1%

자살골
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01985094559
Minimum0
Maximum5
Zeros227623
Zeros (%)98.1%
Memory size1.8 MiB
2021-01-27T18:10:37.958899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1436870329
Coefficient of variation (CV)7.238296646
Kurtosis81.04266895
Mean0.01985094559
Median Absolute Deviation (MAD)0
Skewness7.92133719
Sum4608
Variance0.02064596342
MonotocityNot monotonic
2021-01-27T18:10:38.032372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0227623
98.1%
14431
 
1.9%
261
 
< 0.1%
37
 
< 0.1%
46
 
< 0.1%
52
 
< 0.1%
ValueCountFrequency (%)
0227623
98.1%
14431
 
1.9%
261
 
< 0.1%
37
 
< 0.1%
46
 
< 0.1%
ValueCountFrequency (%)
52
 
< 0.1%
46
 
< 0.1%
37
 
< 0.1%
261
 
< 0.1%
14431
1.9%

헤딩슛
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5888726145
Minimum0
Maximum9
Zeros139847
Zeros (%)60.2%
Memory size1.8 MiB
2021-01-27T18:10:38.104472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8849557559
Coefficient of variation (CV)1.502796588
Kurtosis4.365792409
Mean0.5888726145
Median Absolute Deviation (MAD)0
Skewness1.847798622
Sum136695
Variance0.7831466898
MonotocityNot monotonic
2021-01-27T18:10:38.175351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0139847
60.2%
161076
26.3%
221789
 
9.4%
36675
 
2.9%
41971
 
0.8%
5564
 
0.2%
6155
 
0.1%
743
 
< 0.1%
89
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
0139847
60.2%
161076
26.3%
221789
 
9.4%
36675
 
2.9%
41971
 
0.8%
ValueCountFrequency (%)
91
 
< 0.1%
89
 
< 0.1%
743
 
< 0.1%
6155
 
0.1%
5564
0.2%

헤딩골
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1174988153
Minimum0
Maximum5
Zeros207160
Zeros (%)89.2%
Memory size1.8 MiB
2021-01-27T18:10:38.245593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3539193205
Coefficient of variation (CV)3.012109693
Kurtosis11.39663604
Mean0.1174988153
Median Absolute Deviation (MAD)0
Skewness3.201499107
Sum27275
Variance0.1252588854
MonotocityNot monotonic
2021-01-27T18:10:38.322490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0207160
89.2%
122849
 
9.8%
21950
 
0.8%
3159
 
0.1%
411
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
0207160
89.2%
122849
 
9.8%
21950
 
0.8%
3159
 
0.1%
411
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
411
 
< 0.1%
3159
 
0.1%
21950
 
0.8%
122849
9.8%

프리킥
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1095851463
Minimum0
Maximum5
Zeros208477
Zeros (%)89.8%
Memory size1.8 MiB
2021-01-27T18:10:38.396018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3379428165
Coefficient of variation (CV)3.083837802
Kurtosis11.50276087
Mean0.1095851463
Median Absolute Deviation (MAD)0
Skewness3.231540293
Sum25438
Variance0.1142053472
MonotocityNot monotonic
2021-01-27T18:10:38.473524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0208477
89.8%
122000
 
9.5%
21533
 
0.7%
3109
 
< 0.1%
410
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
0208477
89.8%
122000
 
9.5%
21533
 
0.7%
3109
 
< 0.1%
410
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
410
 
< 0.1%
3109
 
< 0.1%
21533
 
0.7%
122000
9.5%

프리킥골
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
229425 
1
 
2687
2
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters232130
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0229425
98.8%
12687
 
1.2%
218
 
< 0.1%
2021-01-27T18:10:38.637417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-27T18:10:38.690261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0229425
98.8%
12687
 
1.2%
218
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0229425
98.8%
12687
 
1.2%
218
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number232130
100.0%

Most frequent character per category

ValueCountFrequency (%)
0229425
98.8%
12687
 
1.2%
218
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common232130
100.0%

Most frequent character per script

ValueCountFrequency (%)
0229425
98.8%
12687
 
1.2%
218
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII232130
100.0%

Most frequent character per block

ValueCountFrequency (%)
0229425
98.8%
12687
 
1.2%
218
 
< 0.1%

중거리슛
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.483095679
Minimum0
Maximum21
Zeros67168
Zeros (%)28.9%
Memory size1.8 MiB
2021-01-27T18:10:38.753107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum21
Range21
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.447179181
Coefficient of variation (CV)0.9757827506
Kurtosis2.842350789
Mean1.483095679
Median Absolute Deviation (MAD)1
Skewness1.310197587
Sum344271
Variance2.094327583
MonotocityNot monotonic
2021-01-27T18:10:38.837102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
169761
30.1%
067168
28.9%
247698
20.5%
326132
 
11.3%
412342
 
5.3%
55392
 
2.3%
62164
 
0.9%
7875
 
0.4%
8354
 
0.2%
9120
 
0.1%
Other values (9)124
 
0.1%
ValueCountFrequency (%)
067168
28.9%
169761
30.1%
247698
20.5%
326132
 
11.3%
412342
 
5.3%
ValueCountFrequency (%)
212
< 0.1%
191
< 0.1%
171
< 0.1%
162
< 0.1%
141
< 0.1%

중거리골
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2631585749
Minimum0
Maximum12
Zeros180282
Zeros (%)77.7%
Memory size1.8 MiB
2021-01-27T18:10:38.913461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5381884832
Coefficient of variation (CV)2.04511095
Kurtosis8.85733841
Mean0.2631585749
Median Absolute Deviation (MAD)0
Skewness2.397230308
Sum61087
Variance0.2896468435
MonotocityNot monotonic
2021-01-27T18:10:38.994039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0180282
77.7%
143994
 
19.0%
26770
 
2.9%
3889
 
0.4%
4128
 
0.1%
547
 
< 0.1%
612
 
< 0.1%
73
 
< 0.1%
92
 
< 0.1%
82
 
< 0.1%
ValueCountFrequency (%)
0180282
77.7%
143994
 
19.0%
26770
 
2.9%
3889
 
0.4%
4128
 
0.1%
ValueCountFrequency (%)
121
 
< 0.1%
92
 
< 0.1%
82
 
< 0.1%
73
 
< 0.1%
612
< 0.1%

패널티킥
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
219472 
1
 
12255
2
 
395
3
 
7
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters232130
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0219472
94.5%
112255
 
5.3%
2395
 
0.2%
37
 
< 0.1%
41
 
< 0.1%
2021-01-27T18:10:39.161434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-27T18:10:39.215976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0219472
94.5%
112255
 
5.3%
2395
 
0.2%
37
 
< 0.1%
41
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0219472
94.5%
112255
 
5.3%
2395
 
0.2%
37
 
< 0.1%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number232130
100.0%

Most frequent character per category

ValueCountFrequency (%)
0219472
94.5%
112255
 
5.3%
2395
 
0.2%
37
 
< 0.1%
41
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common232130
100.0%

Most frequent character per script

ValueCountFrequency (%)
0219472
94.5%
112255
 
5.3%
2395
 
0.2%
37
 
< 0.1%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII232130
100.0%

Most frequent character per block

ValueCountFrequency (%)
0219472
94.5%
112255
 
5.3%
2395
 
0.2%
37
 
< 0.1%
41
 
< 0.1%

패널티골
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
222501 
1
 
9397
2
 
228
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters232130
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0222501
95.9%
19397
 
4.0%
2228
 
0.1%
34
 
< 0.1%
2021-01-27T18:10:39.369673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-27T18:10:40.430571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0222501
95.9%
19397
 
4.0%
2228
 
0.1%
34
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0222501
95.9%
19397
 
4.0%
2228
 
0.1%
34
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number232130
100.0%

Most frequent character per category

ValueCountFrequency (%)
0222501
95.9%
19397
 
4.0%
2228
 
0.1%
34
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common232130
100.0%

Most frequent character per script

ValueCountFrequency (%)
0222501
95.9%
19397
 
4.0%
2228
 
0.1%
34
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII232130
100.0%

Most frequent character per block

ValueCountFrequency (%)
0222501
95.9%
19397
 
4.0%
2228
 
0.1%
34
 
< 0.1%

날짜
Categorical

HIGH CARDINALITY
UNIFORM

Distinct219007
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2021-01-17T15:53:06
 
5
2021-01-12T23:42:22
 
4
2021-01-17T19:55:21
 
4
2021-01-17T20:54:37
 
4
2021-01-13T21:50:07
 
4
Other values (219002)
232109 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters4410470
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique206558 ?
Unique (%)89.0%

Sample

1st row2021-01-23T04:00:32
2nd row2021-01-23T03:49:12
3rd row2021-01-23T03:38:04
4th row2021-01-23T03:25:26
5th row2021-01-23T03:07:50
ValueCountFrequency (%)
2021-01-17T15:53:065
 
< 0.1%
2021-01-12T23:42:224
 
< 0.1%
2021-01-17T19:55:214
 
< 0.1%
2021-01-17T20:54:374
 
< 0.1%
2021-01-13T21:50:074
 
< 0.1%
2021-01-09T16:48:314
 
< 0.1%
2021-01-17T20:48:494
 
< 0.1%
2021-01-21T15:26:054
 
< 0.1%
2021-01-18T16:14:024
 
< 0.1%
2021-01-17T16:22:384
 
< 0.1%
Other values (218997)232089
> 99.9%
2021-01-27T18:10:41.249897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-01-17t15:53:065
 
< 0.1%
2021-01-17t19:55:214
 
< 0.1%
2021-01-21t16:43:174
 
< 0.1%
2021-01-09t20:03:114
 
< 0.1%
2021-01-17t20:48:494
 
< 0.1%
2021-01-09t16:48:314
 
< 0.1%
2021-01-19t16:49:124
 
< 0.1%
2021-01-12t23:42:224
 
< 0.1%
2021-01-19t22:05:234
 
< 0.1%
2021-01-18t18:02:514
 
< 0.1%
Other values (218997)232089
> 99.9%

Most occurring characters

ValueCountFrequency (%)
1834235
18.9%
2805586
18.3%
0761375
17.3%
-464260
10.5%
:464260
10.5%
T232130
 
5.3%
3182163
 
4.1%
5161011
 
3.7%
4157349
 
3.6%
991113
 
2.1%
Other values (3)256988
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3249820
73.7%
Dash Punctuation464260
 
10.5%
Other Punctuation464260
 
10.5%
Uppercase Letter232130
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
1834235
25.7%
2805586
24.8%
0761375
23.4%
3182163
 
5.6%
5161011
 
5.0%
4157349
 
4.8%
991113
 
2.8%
687842
 
2.7%
786967
 
2.7%
882179
 
2.5%
ValueCountFrequency (%)
-464260
100.0%
ValueCountFrequency (%)
T232130
100.0%
ValueCountFrequency (%)
:464260
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4178340
94.7%
Latin232130
 
5.3%

Most frequent character per script

ValueCountFrequency (%)
1834235
20.0%
2805586
19.3%
0761375
18.2%
-464260
11.1%
:464260
11.1%
3182163
 
4.4%
5161011
 
3.9%
4157349
 
3.8%
991113
 
2.2%
687842
 
2.1%
Other values (2)169146
 
4.0%
ValueCountFrequency (%)
T232130
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4410470
100.0%

Most frequent character per block

ValueCountFrequency (%)
1834235
18.9%
2805586
18.3%
0761375
17.3%
-464260
10.5%
:464260
10.5%
T232130
 
5.3%
3182163
 
4.1%
5161011
 
3.7%
4157349
 
3.6%
991113
 
2.1%
Other values (3)256988
 
5.8%

유저이름
Categorical

HIGH CARDINALITY

Distinct28397
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
다9리
 
307
WAAC초인
 
275
Unvary유기정
 
217
볼빨간개간로
 
207
전철루
 
205
Other values (28392)
230919 

Length

Max length16
Median length6
Mean length6.056153879
Min length2

Characters and Unicode

Total characters1405815
Distinct characters2039
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13955 ?
Unique (%)6.0%

Sample

1st rowTeamKoreaNo1
2nd rowSaddlerHS
3rd row워리어
4th row울산이창욱
5th rowTeamKoreaNo1
ValueCountFrequency (%)
다9리307
 
0.1%
WAAC초인275
 
0.1%
Unvary유기정217
 
0.1%
볼빨간개간로207
 
0.1%
전철루205
 
0.1%
형욱킬러195
 
0.1%
쏘댸장194
 
0.1%
놈코어지성189
 
0.1%
CrazyWin이호185
 
0.1%
lIIlIllIlIIlIllI183
 
0.1%
Other values (28387)229973
99.1%
2021-01-27T18:10:41.524981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
다9리307
 
0.1%
waac초인275
 
0.1%
unvary유기정217
 
0.1%
볼빨간개간로207
 
0.1%
전철루205
 
0.1%
형욱킬러195
 
0.1%
쏘댸장194
 
0.1%
놈코어지성189
 
0.1%
crazywin이호185
 
0.1%
liililliliililli183
 
0.1%
Other values (28387)229973
99.1%

Most occurring characters

ValueCountFrequency (%)
a34788
 
2.5%
e32485
 
2.3%
n30995
 
2.2%
i29805
 
2.1%
26518
 
1.9%
o25926
 
1.8%
l22083
 
1.6%
r19511
 
1.4%
s18372
 
1.3%
A17091
 
1.2%
Other values (2029)1148241
81.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter796293
56.6%
Lowercase Letter345980
24.6%
Uppercase Letter215860
 
15.4%
Decimal Number47682
 
3.4%

Most frequent character per category

ValueCountFrequency (%)
26518
 
3.3%
16621
 
2.1%
12543
 
1.6%
9473
 
1.2%
8913
 
1.1%
7682
 
1.0%
7344
 
0.9%
7204
 
0.9%
7197
 
0.9%
7100
 
0.9%
Other values (1967)685698
86.1%
ValueCountFrequency (%)
A17091
 
7.9%
T13971
 
6.5%
S13757
 
6.4%
F13481
 
6.2%
I13040
 
6.0%
E12874
 
6.0%
N12374
 
5.7%
C11664
 
5.4%
R9806
 
4.5%
L9399
 
4.4%
Other values (16)88403
41.0%
ValueCountFrequency (%)
a34788
 
10.1%
e32485
 
9.4%
n30995
 
9.0%
i29805
 
8.6%
o25926
 
7.5%
l22083
 
6.4%
r19511
 
5.6%
s18372
 
5.3%
t16274
 
4.7%
u16127
 
4.7%
Other values (16)99614
28.8%
ValueCountFrequency (%)
19594
20.1%
29257
19.4%
09081
19.0%
94012
8.4%
73379
 
7.1%
33020
 
6.3%
42860
 
6.0%
52343
 
4.9%
82277
 
4.8%
61859
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul796293
56.6%
Latin561840
40.0%
Common47682
 
3.4%

Most frequent character per script

ValueCountFrequency (%)
26518
 
3.3%
16621
 
2.1%
12543
 
1.6%
9473
 
1.2%
8913
 
1.1%
7682
 
1.0%
7344
 
0.9%
7204
 
0.9%
7197
 
0.9%
7100
 
0.9%
Other values (1967)685698
86.1%
ValueCountFrequency (%)
a34788
 
6.2%
e32485
 
5.8%
n30995
 
5.5%
i29805
 
5.3%
o25926
 
4.6%
l22083
 
3.9%
r19511
 
3.5%
s18372
 
3.3%
A17091
 
3.0%
t16274
 
2.9%
Other values (42)314510
56.0%
ValueCountFrequency (%)
19594
20.1%
29257
19.4%
09081
19.0%
94012
8.4%
73379
 
7.1%
33020
 
6.3%
42860
 
6.0%
52343
 
4.9%
82277
 
4.8%
61859
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul796293
56.6%
ASCII609522
43.4%

Most frequent character per block

ValueCountFrequency (%)
a34788
 
5.7%
e32485
 
5.3%
n30995
 
5.1%
i29805
 
4.9%
o25926
 
4.3%
l22083
 
3.6%
r19511
 
3.2%
s18372
 
3.0%
A17091
 
2.8%
t16274
 
2.7%
Other values (52)362192
59.4%
ValueCountFrequency (%)
26518
 
3.3%
16621
 
2.1%
12543
 
1.6%
9473
 
1.2%
8913
 
1.1%
7682
 
1.0%
7344
 
0.9%
7204
 
0.9%
7197
 
0.9%
7100
 
0.9%
Other values (1967)685698
86.1%

경기결과
Categorical

Distinct3
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size1.8 MiB
99437 
98990 
33701 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters232128
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
99437
42.8%
98990
42.6%
33701
 
14.5%
(Missing)2
 
< 0.1%
2021-01-27T18:10:41.712286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-27T18:10:41.763235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
99437
42.8%
98990
42.6%
33701
 
14.5%

Most occurring characters

ValueCountFrequency (%)
99437
42.8%
98990
42.6%
33701
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter232128
100.0%

Most frequent character per category

ValueCountFrequency (%)
99437
42.8%
98990
42.6%
33701
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul232128
100.0%

Most frequent character per script

ValueCountFrequency (%)
99437
42.8%
98990
42.6%
33701
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul232128
100.0%

Most frequent character per block

ValueCountFrequency (%)
99437
42.8%
98990
42.6%
33701
 
14.5%

파울
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6659199586
Minimum0
Maximum11
Zeros123275
Zeros (%)53.1%
Memory size1.8 MiB
2021-01-27T18:10:41.823752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8612801222
Coefficient of variation (CV)1.293368837
Kurtosis2.7664504
Mean0.6659199586
Median Absolute Deviation (MAD)0
Skewness1.463643002
Sum154580
Variance0.7418034489
MonotocityNot monotonic
2021-01-27T18:10:41.898972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0123275
53.1%
174386
32.0%
225749
 
11.1%
36743
 
2.9%
41556
 
0.7%
5325
 
0.1%
668
 
< 0.1%
717
 
< 0.1%
810
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
0123275
53.1%
174386
32.0%
225749
 
11.1%
36743
 
2.9%
41556
 
0.7%
ValueCountFrequency (%)
111
 
< 0.1%
810
 
< 0.1%
717
 
< 0.1%
668
 
< 0.1%
5325
0.1%

부상
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
199880 
1
29877 
2
 
2267
3
 
99
4
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters232130
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0199880
86.1%
129877
 
12.9%
22267
 
1.0%
399
 
< 0.1%
47
 
< 0.1%
2021-01-27T18:10:42.087710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-27T18:10:42.141238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0199880
86.1%
129877
 
12.9%
22267
 
1.0%
399
 
< 0.1%
47
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0199880
86.1%
129877
 
12.9%
22267
 
1.0%
399
 
< 0.1%
47
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number232130
100.0%

Most frequent character per category

ValueCountFrequency (%)
0199880
86.1%
129877
 
12.9%
22267
 
1.0%
399
 
< 0.1%
47
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common232130
100.0%

Most frequent character per script

ValueCountFrequency (%)
0199880
86.1%
129877
 
12.9%
22267
 
1.0%
399
 
< 0.1%
47
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII232130
100.0%

Most frequent character per block

ValueCountFrequency (%)
0199880
86.1%
129877
 
12.9%
22267
 
1.0%
399
 
< 0.1%
47
 
< 0.1%

레드카드
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
230287 
1
 
1801
2
 
37
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters232130
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0230287
99.2%
11801
 
0.8%
237
 
< 0.1%
35
 
< 0.1%
2021-01-27T18:10:42.293491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-27T18:10:42.348170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0230287
99.2%
11801
 
0.8%
237
 
< 0.1%
35
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0230287
99.2%
11801
 
0.8%
237
 
< 0.1%
35
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number232130
100.0%

Most frequent character per category

ValueCountFrequency (%)
0230287
99.2%
11801
 
0.8%
237
 
< 0.1%
35
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common232130
100.0%

Most frequent character per script

ValueCountFrequency (%)
0230287
99.2%
11801
 
0.8%
237
 
< 0.1%
35
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII232130
100.0%

Most frequent character per block

ValueCountFrequency (%)
0230287
99.2%
11801
 
0.8%
237
 
< 0.1%
35
 
< 0.1%

옐로카드
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06031964847
Minimum0
Maximum6
Zeros219603
Zeros (%)94.6%
Memory size1.8 MiB
2021-01-27T18:10:42.409370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2692426548
Coefficient of variation (CV)4.463597876
Kurtosis41.00774113
Mean0.06031964847
Median Absolute Deviation (MAD)0
Skewness5.45973994
Sum14002
Variance0.07249160718
MonotocityNot monotonic
2021-01-27T18:10:42.478274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0219603
94.6%
111329
 
4.9%
2982
 
0.4%
3172
 
0.1%
432
 
< 0.1%
57
 
< 0.1%
65
 
< 0.1%
ValueCountFrequency (%)
0219603
94.6%
111329
 
4.9%
2982
 
0.4%
3172
 
0.1%
432
 
< 0.1%
ValueCountFrequency (%)
65
 
< 0.1%
57
 
< 0.1%
432
 
< 0.1%
3172
 
0.1%
2982
0.4%

드리블횟수
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct188
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.63070262
Minimum0
Maximum198
Zeros2496
Zeros (%)1.1%
Memory size1.8 MiB
2021-01-27T18:10:42.576950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48
Q171
median82
Q394
95-th percentile116
Maximum198
Range198
Interquartile range (IQR)23

Descriptive statistics

Standard deviation21.64414555
Coefficient of variation (CV)0.2651471181
Kurtosis2.548375563
Mean81.63070262
Median Absolute Deviation (MAD)11
Skewness-0.4945039474
Sum18948935
Variance468.4690365
MonotocityNot monotonic
2021-01-27T18:10:42.688993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
825675
 
2.4%
795590
 
2.4%
835588
 
2.4%
775564
 
2.4%
805548
 
2.4%
815529
 
2.4%
765505
 
2.4%
845473
 
2.4%
785408
 
2.3%
855269
 
2.3%
Other values (178)176981
76.2%
ValueCountFrequency (%)
02496
1.1%
184
 
< 0.1%
250
 
< 0.1%
330
 
< 0.1%
441
 
< 0.1%
ValueCountFrequency (%)
1981
< 0.1%
1951
< 0.1%
1921
< 0.1%
1901
< 0.1%
1891
< 0.1%

코너킥횟수
Real number (ℝ≥0)

ZEROS

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.329164692
Minimum0
Maximum13
Zeros75361
Zeros (%)32.5%
Memory size1.8 MiB
2021-01-27T18:10:42.784305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.339432489
Coefficient of variation (CV)1.007724999
Kurtosis1.822806332
Mean1.329164692
Median Absolute Deviation (MAD)1
Skewness1.215477634
Sum308539
Variance1.794079391
MonotocityNot monotonic
2021-01-27T18:10:42.871775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
075361
32.5%
171168
30.7%
245812
19.7%
323162
 
10.0%
410235
 
4.4%
54133
 
1.8%
61490
 
0.6%
7536
 
0.2%
8160
 
0.1%
955
 
< 0.1%
Other values (4)18
 
< 0.1%
ValueCountFrequency (%)
075361
32.5%
171168
30.7%
245812
19.7%
323162
 
10.0%
410235
 
4.4%
ValueCountFrequency (%)
131
 
< 0.1%
122
 
< 0.1%
112
 
< 0.1%
1013
 
< 0.1%
955
< 0.1%

점유율
Real number (ℝ≥0)

ZEROS

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.63400681
Minimum0
Maximum100
Zeros2346
Zeros (%)1.0%
Memory size1.8 MiB
2021-01-27T18:10:42.974370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39
Q146
median50
Q354
95-th percentile60
Maximum100
Range100
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.053175597
Coefficient of variation (CV)0.1622511684
Kurtosis13.61072529
Mean49.63400681
Median Absolute Deviation (MAD)4
Skewness-2.143394206
Sum11521542
Variance64.8536372
MonotocityNot monotonic
2021-01-27T18:10:43.088725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5015457
 
6.7%
5114944
 
6.4%
4914935
 
6.4%
5214408
 
6.2%
4814123
 
6.1%
5313379
 
5.8%
4713169
 
5.7%
5412366
 
5.3%
4611936
 
5.1%
5510897
 
4.7%
Other values (89)96516
41.6%
ValueCountFrequency (%)
02346
1.0%
21
 
< 0.1%
32
 
< 0.1%
44
 
< 0.1%
54
 
< 0.1%
ValueCountFrequency (%)
10064
< 0.1%
991
 
< 0.1%
983
 
< 0.1%
971
 
< 0.1%
965
 
< 0.1%

오프사이드횟수
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3557230862
Minimum0
Maximum6
Zeros164372
Zeros (%)70.8%
Memory size1.8 MiB
2021-01-27T18:10:43.179289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6150053616
Coefficient of variation (CV)1.728887962
Kurtosis3.851085398
Mean0.3557230862
Median Absolute Deviation (MAD)0
Skewness1.843120127
Sum82574
Variance0.3782315948
MonotocityNot monotonic
2021-01-27T18:10:43.252415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0164372
70.8%
155077
 
23.7%
210846
 
4.7%
31579
 
0.7%
4216
 
0.1%
536
 
< 0.1%
64
 
< 0.1%
ValueCountFrequency (%)
0164372
70.8%
155077
 
23.7%
210846
 
4.7%
31579
 
0.7%
4216
 
0.1%
ValueCountFrequency (%)
64
 
< 0.1%
536
 
< 0.1%
4216
 
0.1%
31579
 
0.7%
210846
4.7%

평점
Real number (ℝ≥0)

ZEROS

Distinct797
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.073216137
Minimum0
Maximum6.54444
Zeros2705
Zeros (%)1.2%
Memory size1.8 MiB
2021-01-27T18:10:43.352969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.39444
Q13.83333
median4.10556
Q34.35556
95-th percentile4.88889
Maximum6.54444
Range6.54444
Interquartile range (IQR)0.52223

Descriptive statistics

Standard deviation0.6161024775
Coefficient of variation (CV)0.151257006
Kurtosis20.26226679
Mean4.073216137
Median Absolute Deviation (MAD)0.26112
Skewness-3.110386624
Sum945515.6618
Variance0.3795822627
MonotocityNot monotonic
2021-01-27T18:10:43.460522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02705
 
1.2%
4.216671497
 
0.6%
4.177781464
 
0.6%
4.222221449
 
0.6%
4.21433
 
0.6%
4.205561410
 
0.6%
4.244441410
 
0.6%
4.233331410
 
0.6%
4.227781404
 
0.6%
4.188891401
 
0.6%
Other values (787)216547
93.3%
ValueCountFrequency (%)
02705
1.2%
2.172221
 
< 0.1%
2.383331
 
< 0.1%
2.427781
 
< 0.1%
2.461111
 
< 0.1%
ValueCountFrequency (%)
6.544441
< 0.1%
6.422221
< 0.1%
6.322221
< 0.1%
6.216671
< 0.1%
6.194441
< 0.1%

선수1
Categorical

HIGH CARDINALITY
MISSING

Distinct4632
Distinct (%)2.0%
Missing2466
Missing (%)1.1%
Memory size1.8 MiB
파올로 말디니
22208 
미하엘 발락
20722 
에마뉘엘 프티
 
12188
파트리크 비에이라
 
9353
마르셀 드사이
 
8812
Other values (4627)
156381 

Length

Max length12
Median length7
Mean length6.648734673
Min length1

Characters and Unicode

Total characters1526975
Distinct characters717
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1582 ?
Unique (%)0.7%

Sample

1st row김두현
2nd row에마뉘엘 프티
3rd row미하엘 발락
4th row파벨 네드베드
5th row김두현
ValueCountFrequency (%)
파올로 말디니22208
 
9.6%
미하엘 발락20722
 
8.9%
에마뉘엘 프티12188
 
5.3%
파트리크 비에이라9353
 
4.0%
마르셀 드사이8812
 
3.8%
크리스티아누 호날두5256
 
2.3%
솔 캠벨4534
 
2.0%
클라렌스 세이도르프4429
 
1.9%
A. 델피에로4322
 
1.9%
파벨 네드베드4109
 
1.8%
Other values (4622)133731
57.6%
2021-01-27T18:10:43.686316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
파올로22218
 
5.1%
말디니22208
 
5.1%
미하엘20777
 
4.8%
발락20722
 
4.8%
프티12188
 
2.8%
에마뉘엘12188
 
2.8%
파트리크9368
 
2.2%
비에이라9357
 
2.2%
마르셀8848
 
2.0%
드사이8812
 
2.0%
Other values (5220)287711
66.2%

Most occurring characters

ValueCountFrequency (%)
204733
 
13.4%
51807
 
3.4%
43874
 
2.9%
43814
 
2.9%
40412
 
2.6%
40221
 
2.6%
39729
 
2.6%
39672
 
2.6%
37047
 
2.4%
35941
 
2.4%
Other values (707)949725
62.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter1297258
85.0%
Space Separator204733
 
13.4%
Uppercase Letter11607
 
0.8%
Other Punctuation11583
 
0.8%
Dash Punctuation939
 
0.1%
Lowercase Letter855
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
51807
 
4.0%
43874
 
3.4%
43814
 
3.4%
40412
 
3.1%
40221
 
3.1%
39729
 
3.1%
39672
 
3.1%
37047
 
2.9%
35941
 
2.8%
31732
 
2.4%
Other values (674)893009
68.8%
ValueCountFrequency (%)
A4872
42.0%
J1353
 
11.7%
M1128
 
9.7%
Z952
 
8.2%
P805
 
6.9%
R465
 
4.0%
S310
 
2.7%
T288
 
2.5%
H232
 
2.0%
C190
 
1.6%
Other values (19)1012
 
8.7%
ValueCountFrequency (%)
204733
100.0%
ValueCountFrequency (%)
.11583
100.0%
ValueCountFrequency (%)
-939
100.0%
ValueCountFrequency (%)
r855
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1297258
85.0%
Common217255
 
14.2%
Latin12462
 
0.8%

Most frequent character per script

ValueCountFrequency (%)
51807
 
4.0%
43874
 
3.4%
43814
 
3.4%
40412
 
3.1%
40221
 
3.1%
39729
 
3.1%
39672
 
3.1%
37047
 
2.9%
35941
 
2.8%
31732
 
2.4%
Other values (674)893009
68.8%
ValueCountFrequency (%)
A4872
39.1%
J1353
 
10.9%
M1128
 
9.1%
Z952
 
7.6%
r855
 
6.9%
P805
 
6.5%
R465
 
3.7%
S310
 
2.5%
T288
 
2.3%
H232
 
1.9%
Other values (20)1202
 
9.6%
ValueCountFrequency (%)
204733
94.2%
.11583
 
5.3%
-939
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul1297258
85.0%
ASCII229702
 
15.0%
None15
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
51807
 
4.0%
43874
 
3.4%
43814
 
3.4%
40412
 
3.1%
40221
 
3.1%
39729
 
3.1%
39672
 
3.1%
37047
 
2.9%
35941
 
2.8%
31732
 
2.4%
Other values (674)893009
68.8%
ValueCountFrequency (%)
204733
89.1%
.11583
 
5.0%
A4872
 
2.1%
J1353
 
0.6%
M1128
 
0.5%
Z952
 
0.4%
-939
 
0.4%
r855
 
0.4%
P805
 
0.4%
R465
 
0.2%
Other values (19)2017
 
0.9%
ValueCountFrequency (%)
Ș6
40.0%
Ö5
33.3%
İ2
 
13.3%
Ł2
 
13.3%

선수2
Categorical

HIGH CARDINALITY
MISSING

Distinct4680
Distinct (%)2.0%
Missing2493
Missing (%)1.1%
Memory size1.8 MiB
미하엘 발락
21567 
에마뉘엘 프티
 
10975
파트리크 비에이라
 
10888
파벨 네드베드
 
9309
크리스티아누 호날두
 
6960
Other values (4675)
169938 

Length

Max length13
Median length7
Mean length6.63758889
Min length1

Characters and Unicode

Total characters1524236
Distinct characters712
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1542 ?
Unique (%)0.7%

Sample

1st row홍명보
2nd row파벨 네드베드
3rd row파벨 네드베드
4th row에마뉘엘 프티
5th row홍명보
ValueCountFrequency (%)
미하엘 발락21567
 
9.3%
에마뉘엘 프티10975
 
4.7%
파트리크 비에이라10888
 
4.7%
파벨 네드베드9309
 
4.0%
크리스티아누 호날두6960
 
3.0%
스티븐 제라드6378
 
2.7%
호나우두5828
 
2.5%
파올로 말디니5178
 
2.2%
프랭크 램파드4937
 
2.1%
에르난 크레스포3830
 
1.6%
Other values (4670)143787
61.9%
2021-01-27T18:10:43.905261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미하엘21622
 
5.0%
발락21567
 
5.0%
에마뉘엘10975
 
2.6%
프티10975
 
2.6%
파트리크10898
 
2.5%
비에이라10891
 
2.5%
파벨9313
 
2.2%
네드베드9309
 
2.2%
크리스티아누6960
 
1.6%
호날두6960
 
1.6%
Other values (5290)309353
72.1%

Most occurring characters

ValueCountFrequency (%)
199186
 
13.1%
57627
 
3.8%
49479
 
3.2%
47179
 
3.1%
40384
 
2.6%
39359
 
2.6%
38391
 
2.5%
37551
 
2.5%
36952
 
2.4%
35212
 
2.3%
Other values (702)942916
61.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter1303548
85.5%
Space Separator199186
 
13.1%
Uppercase Letter9837
 
0.6%
Other Punctuation9813
 
0.6%
Lowercase Letter1047
 
0.1%
Dash Punctuation805
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
57627
 
4.4%
49479
 
3.8%
47179
 
3.6%
40384
 
3.1%
39359
 
3.0%
38391
 
2.9%
37551
 
2.9%
36952
 
2.8%
35212
 
2.7%
34029
 
2.6%
Other values (670)887385
68.1%
ValueCountFrequency (%)
M1665
16.9%
A1480
15.0%
J1461
14.9%
Z1194
12.1%
P1135
11.5%
R564
 
5.7%
T342
 
3.5%
S333
 
3.4%
F249
 
2.5%
H220
 
2.2%
Other values (18)1194
12.1%
ValueCountFrequency (%)
199186
100.0%
ValueCountFrequency (%)
.9813
100.0%
ValueCountFrequency (%)
r1047
100.0%
ValueCountFrequency (%)
-805
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1303548
85.5%
Common209804
 
13.8%
Latin10884
 
0.7%

Most frequent character per script

ValueCountFrequency (%)
57627
 
4.4%
49479
 
3.8%
47179
 
3.6%
40384
 
3.1%
39359
 
3.0%
38391
 
2.9%
37551
 
2.9%
36952
 
2.8%
35212
 
2.7%
34029
 
2.6%
Other values (670)887385
68.1%
ValueCountFrequency (%)
M1665
15.3%
A1480
13.6%
J1461
13.4%
Z1194
11.0%
P1135
10.4%
r1047
9.6%
R564
 
5.2%
T342
 
3.1%
S333
 
3.1%
F249
 
2.3%
Other values (19)1414
13.0%
ValueCountFrequency (%)
199186
94.9%
.9813
 
4.7%
-805
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul1303548
85.5%
ASCII220673
 
14.5%
None15
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
57627
 
4.4%
49479
 
3.8%
47179
 
3.6%
40384
 
3.1%
39359
 
3.0%
38391
 
2.9%
37551
 
2.9%
36952
 
2.8%
35212
 
2.7%
34029
 
2.6%
Other values (670)887385
68.1%
ValueCountFrequency (%)
199186
90.3%
.9813
 
4.4%
M1665
 
0.8%
A1480
 
0.7%
J1461
 
0.7%
Z1194
 
0.5%
P1135
 
0.5%
r1047
 
0.5%
-805
 
0.4%
R564
 
0.3%
Other values (19)2323
 
1.1%
ValueCountFrequency (%)
Ș12
80.0%
Ł2
 
13.3%
İ1
 
6.7%

선수3
Categorical

HIGH CARDINALITY
MISSING

Distinct4897
Distinct (%)2.1%
Missing2498
Missing (%)1.1%
Memory size1.8 MiB
미하엘 발락
 
12911
파벨 네드베드
 
10821
호나우두
 
8940
크리스티아누 호날두
 
8830
스티븐 제라드
 
8630
Other values (4892)
179500 

Length

Max length13
Median length7
Mean length6.529181473
Min length1

Characters and Unicode

Total characters1499309
Distinct characters713
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1620 ?
Unique (%)0.7%

Sample

1st row조원희
2nd row크리스티아누 호날두
3rd row스티븐 제라드
4th row미하엘 발락
5th row조원희
ValueCountFrequency (%)
미하엘 발락12911
 
5.6%
파벨 네드베드10821
 
4.7%
호나우두8940
 
3.9%
크리스티아누 호날두8830
 
3.8%
스티븐 제라드8630
 
3.7%
에마뉘엘 프티5867
 
2.5%
에르난 크레스포5858
 
2.5%
프랭크 램파드5210
 
2.2%
파트리크 비에이라4867
 
2.1%
야프 스탐3923
 
1.7%
Other values (4887)153775
66.2%
2021-01-27T18:10:44.134760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미하엘12969
 
3.1%
발락12911
 
3.1%
파벨10824
 
2.6%
네드베드10821
 
2.6%
호나우두8949
 
2.1%
크리스티아누8830
 
2.1%
호날두8830
 
2.1%
스티븐8664
 
2.1%
제라드8631
 
2.0%
크레스포5878
 
1.4%
Other values (5507)324793
76.9%

Most occurring characters

ValueCountFrequency (%)
192468
 
12.8%
66085
 
4.4%
53415
 
3.6%
47165
 
3.1%
41395
 
2.8%
34196
 
2.3%
33636
 
2.2%
32406
 
2.2%
32071
 
2.1%
31378
 
2.1%
Other values (703)935094
62.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter1283955
85.6%
Space Separator192468
 
12.8%
Uppercase Letter10392
 
0.7%
Other Punctuation10366
 
0.7%
Lowercase Letter1121
 
0.1%
Dash Punctuation1007
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
66085
 
5.1%
53415
 
4.2%
47165
 
3.7%
41395
 
3.2%
34196
 
2.7%
33636
 
2.6%
32406
 
2.5%
32071
 
2.5%
31378
 
2.4%
27665
 
2.2%
Other values (672)884543
68.9%
ValueCountFrequency (%)
M1799
17.3%
Z1693
16.3%
J1644
15.8%
A1039
10.0%
P978
9.4%
R559
 
5.4%
T451
 
4.3%
S365
 
3.5%
C264
 
2.5%
F241
 
2.3%
Other values (17)1359
13.1%
ValueCountFrequency (%)
192468
100.0%
ValueCountFrequency (%)
.10366
100.0%
ValueCountFrequency (%)
r1121
100.0%
ValueCountFrequency (%)
-1007
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1283955
85.6%
Common203841
 
13.6%
Latin11513
 
0.8%

Most frequent character per script

ValueCountFrequency (%)
66085
 
5.1%
53415
 
4.2%
47165
 
3.7%
41395
 
3.2%
34196
 
2.7%
33636
 
2.6%
32406
 
2.5%
32071
 
2.5%
31378
 
2.4%
27665
 
2.2%
Other values (672)884543
68.9%
ValueCountFrequency (%)
M1799
15.6%
Z1693
14.7%
J1644
14.3%
r1121
9.7%
A1039
9.0%
P978
8.5%
R559
 
4.9%
T451
 
3.9%
S365
 
3.2%
C264
 
2.3%
Other values (18)1600
13.9%
ValueCountFrequency (%)
192468
94.4%
.10366
 
5.1%
-1007
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul1283955
85.6%
ASCII215331
 
14.4%
None23
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
66085
 
5.1%
53415
 
4.2%
47165
 
3.7%
41395
 
3.2%
34196
 
2.7%
33636
 
2.6%
32406
 
2.5%
32071
 
2.5%
31378
 
2.4%
27665
 
2.2%
Other values (672)884543
68.9%
ValueCountFrequency (%)
192468
89.4%
.10366
 
4.8%
M1799
 
0.8%
Z1693
 
0.8%
J1644
 
0.8%
r1121
 
0.5%
A1039
 
0.5%
-1007
 
0.5%
P978
 
0.5%
R559
 
0.3%
Other values (18)2657
 
1.2%
ValueCountFrequency (%)
Ș18
78.3%
Ł3
 
13.0%
Ö2
 
8.7%

선수4
Categorical

HIGH CARDINALITY
MISSING

Distinct4998
Distinct (%)2.2%
Missing2557
Missing (%)1.1%
Memory size1.8 MiB
호나우두
 
10227
크리스티아누 호날두
 
9179
스티븐 제라드
 
7793
파벨 네드베드
 
7135
에르난 크레스포
 
6480
Other values (4993)
188759 

Length

Max length13
Median length7
Mean length6.474607206
Min length2

Characters and Unicode

Total characters1486395
Distinct characters720
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1583 ?
Unique (%)0.7%

Sample

1st row유상철
2nd row티아구 실바
3rd row크리스티아누 호날두
4th row야프 스탐
5th row유상철
ValueCountFrequency (%)
호나우두10227
 
4.4%
크리스티아누 호날두9179
 
4.0%
스티븐 제라드7793
 
3.4%
파벨 네드베드7135
 
3.1%
에르난 크레스포6480
 
2.8%
미하엘 발락5081
 
2.2%
이반 페리시치4220
 
1.8%
프랭크 램파드3458
 
1.5%
제롬 보아텡3275
 
1.4%
가레스 베일3018
 
1.3%
Other values (4988)169707
73.1%
2021-01-27T18:10:44.358192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
호나우두10234
 
2.5%
호날두9179
 
2.2%
크리스티아누9179
 
2.2%
스티븐7831
 
1.9%
제라드7793
 
1.9%
파벨7139
 
1.7%
네드베드7135
 
1.7%
크레스포6496
 
1.6%
에르난6482
 
1.6%
미하엘5160
 
1.2%
Other values (5581)340930
81.6%

Most occurring characters

ValueCountFrequency (%)
187985
 
12.6%
67690
 
4.6%
48245
 
3.2%
45575
 
3.1%
44801
 
3.0%
33256
 
2.2%
32249
 
2.2%
30917
 
2.1%
29966
 
2.0%
29615
 
2.0%
Other values (710)936096
63.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter1271851
85.6%
Space Separator187985
 
12.6%
Uppercase Letter11981
 
0.8%
Other Punctuation11957
 
0.8%
Lowercase Letter1504
 
0.1%
Dash Punctuation1117
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
67690
 
5.3%
48245
 
3.8%
45575
 
3.6%
44801
 
3.5%
33256
 
2.6%
32249
 
2.5%
30917
 
2.4%
29966
 
2.4%
29615
 
2.3%
28546
 
2.2%
Other values (676)880991
69.3%
ValueCountFrequency (%)
Z2307
19.3%
J2044
17.1%
M1362
11.4%
A1168
9.7%
R983
8.2%
P927
7.7%
T686
 
5.7%
S439
 
3.7%
C321
 
2.7%
F273
 
2.3%
Other values (20)1471
12.3%
ValueCountFrequency (%)
187985
100.0%
ValueCountFrequency (%)
r1504
100.0%
ValueCountFrequency (%)
.11957
100.0%
ValueCountFrequency (%)
-1117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1271851
85.6%
Common201059
 
13.5%
Latin13485
 
0.9%

Most frequent character per script

ValueCountFrequency (%)
67690
 
5.3%
48245
 
3.8%
45575
 
3.6%
44801
 
3.5%
33256
 
2.6%
32249
 
2.5%
30917
 
2.4%
29966
 
2.4%
29615
 
2.3%
28546
 
2.2%
Other values (676)880991
69.3%
ValueCountFrequency (%)
Z2307
17.1%
J2044
15.2%
r1504
11.2%
M1362
10.1%
A1168
8.7%
R983
7.3%
P927
6.9%
T686
 
5.1%
S439
 
3.3%
C321
 
2.4%
Other values (21)1744
12.9%
ValueCountFrequency (%)
187985
93.5%
.11957
 
5.9%
-1117
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul1271851
85.6%
ASCII214517
 
14.4%
None27
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
67690
 
5.3%
48245
 
3.8%
45575
 
3.6%
44801
 
3.5%
33256
 
2.6%
32249
 
2.5%
30917
 
2.4%
29966
 
2.4%
29615
 
2.3%
28546
 
2.2%
Other values (676)880991
69.3%
ValueCountFrequency (%)
187985
87.6%
.11957
 
5.6%
Z2307
 
1.1%
J2044
 
1.0%
r1504
 
0.7%
M1362
 
0.6%
A1168
 
0.5%
-1117
 
0.5%
R983
 
0.5%
P927
 
0.4%
Other values (18)3163
 
1.5%
ValueCountFrequency (%)
Ș17
63.0%
Ö5
 
18.5%
Ł2
 
7.4%
É1
 
3.7%
Ó1
 
3.7%
İ1
 
3.7%

선수5
Categorical

HIGH CARDINALITY
MISSING

Distinct5137
Distinct (%)2.2%
Missing2595
Missing (%)1.1%
Memory size1.8 MiB
호나우두
 
9674
크리스티아누 호날두
 
7267
이반 페리시치
 
5537
스티븐 제라드
 
5115
제롬 보아텡
 
4746
Other values (5132)
197196 

Length

Max length15
Median length7
Mean length6.432038687
Min length1

Characters and Unicode

Total characters1476378
Distinct characters724
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1552 ?
Unique (%)0.7%

Sample

1st rowM. 페르난데스
2nd row카일 워커
3rd row호나우두
4th row호나우두
5th row페테르 굴라치
ValueCountFrequency (%)
호나우두9674
 
4.2%
크리스티아누 호날두7267
 
3.1%
이반 페리시치5537
 
2.4%
스티븐 제라드5115
 
2.2%
제롬 보아텡4746
 
2.0%
에르난 크레스포4364
 
1.9%
파벨 네드베드3765
 
1.6%
사미 케디라3727
 
1.6%
가레스 베일3553
 
1.5%
미하엘 발락2963
 
1.3%
Other values (5127)178824
77.0%
2021-01-27T18:10:44.584253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
호나우두9681
 
2.3%
호날두7267
 
1.7%
크리스티아누7267
 
1.7%
이반6219
 
1.5%
페리시치5537
 
1.3%
스티븐5195
 
1.2%
제라드5116
 
1.2%
보아텡5080
 
1.2%
제롬4769
 
1.1%
크레스포4377
 
1.1%
Other values (5708)356108
85.5%

Most occurring characters

ValueCountFrequency (%)
187081
 
12.7%
63432
 
4.3%
47989
 
3.3%
45753
 
3.1%
35778
 
2.4%
35377
 
2.4%
32456
 
2.2%
32279
 
2.2%
25092
 
1.7%
25049
 
1.7%
Other values (714)946092
64.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter1258502
85.2%
Space Separator187081
 
12.7%
Uppercase Letter13798
 
0.9%
Other Punctuation13760
 
0.9%
Lowercase Letter2025
 
0.1%
Dash Punctuation1212
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
63432
 
5.0%
47989
 
3.8%
45753
 
3.6%
35778
 
2.8%
35377
 
2.8%
32456
 
2.6%
32279
 
2.6%
25092
 
2.0%
25049
 
2.0%
24134
 
1.9%
Other values (681)891163
70.8%
ValueCountFrequency (%)
J2659
19.3%
Z2534
18.4%
A1465
10.6%
R1340
9.7%
M1205
8.7%
T929
 
6.7%
P850
 
6.2%
S697
 
5.1%
C358
 
2.6%
F278
 
2.0%
Other values (19)1483
10.7%
ValueCountFrequency (%)
.13760
100.0%
ValueCountFrequency (%)
187081
100.0%
ValueCountFrequency (%)
r2025
100.0%
ValueCountFrequency (%)
-1212
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1258502
85.2%
Common202053
 
13.7%
Latin15823
 
1.1%

Most frequent character per script

ValueCountFrequency (%)
63432
 
5.0%
47989
 
3.8%
45753
 
3.6%
35778
 
2.8%
35377
 
2.8%
32456
 
2.6%
32279
 
2.6%
25092
 
2.0%
25049
 
2.0%
24134
 
1.9%
Other values (681)891163
70.8%
ValueCountFrequency (%)
J2659
16.8%
Z2534
16.0%
r2025
12.8%
A1465
9.3%
R1340
8.5%
M1205
7.6%
T929
 
5.9%
P850
 
5.4%
S697
 
4.4%
C358
 
2.3%
Other values (20)1761
11.1%
ValueCountFrequency (%)
187081
92.6%
.13760
 
6.8%
-1212
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul1258502
85.2%
ASCII217864
 
14.8%
None12
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
187081
85.9%
.13760
 
6.3%
J2659
 
1.2%
Z2534
 
1.2%
r2025
 
0.9%
A1465
 
0.7%
R1340
 
0.6%
-1212
 
0.6%
M1205
 
0.6%
T929
 
0.4%
Other values (18)3654
 
1.7%
ValueCountFrequency (%)
63432
 
5.0%
47989
 
3.8%
45753
 
3.6%
35778
 
2.8%
35377
 
2.8%
32456
 
2.6%
32279
 
2.6%
25092
 
2.0%
25049
 
2.0%
24134
 
1.9%
Other values (681)891163
70.8%
ValueCountFrequency (%)
Ș6
50.0%
Ö3
25.0%
Ó1
 
8.3%
Ł1
 
8.3%
İ1
 
8.3%

선수6
Categorical

HIGH CARDINALITY
MISSING

Distinct5265
Distinct (%)2.3%
Missing2523
Missing (%)1.1%
Memory size1.8 MiB
호나우두
 
7195
제롬 보아텡
 
6174
이반 페리시치
 
5984
사미 케디라
 
4821
크리스티아누 호날두
 
4356
Other values (5260)
201077 

Length

Max length13
Median length7
Mean length6.400758688
Min length1

Characters and Unicode

Total characters1469659
Distinct characters723
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1543 ?
Unique (%)0.7%

Sample

1st row페테르 굴라치
2nd row카밀 글리크
3rd rowZ. 이브라히모비치
4th rowZ. 이브라히모비치
5th row정산
ValueCountFrequency (%)
호나우두7195
 
3.1%
제롬 보아텡6174
 
2.7%
이반 페리시치5984
 
2.6%
사미 케디라4821
 
2.1%
크리스티아누 호날두4356
 
1.9%
티보 쿠르투아4157
 
1.8%
가레스 베일3680
 
1.6%
아르투로 비달3176
 
1.4%
스티븐 제라드3043
 
1.3%
네이마르 Jr.2967
 
1.3%
Other values (5255)184054
79.3%
2021-01-27T18:10:44.817927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
호나우두7203
 
1.7%
이반6692
 
1.6%
보아텡6636
 
1.6%
제롬6207
 
1.5%
페리시치5984
 
1.4%
사미4824
 
1.2%
케디라4824
 
1.2%
호날두4356
 
1.0%
크리스티아누4356
 
1.0%
티보4158
 
1.0%
Other values (5828)362610
86.8%

Most occurring characters

ValueCountFrequency (%)
188243
 
12.8%
59682
 
4.1%
48910
 
3.3%
43855
 
3.0%
37035
 
2.5%
34661
 
2.4%
32256
 
2.2%
29325
 
2.0%
22409
 
1.5%
21893
 
1.5%
Other values (713)951390
64.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter1245287
84.7%
Space Separator188243
 
12.8%
Uppercase Letter15775
 
1.1%
Other Punctuation15741
 
1.1%
Lowercase Letter2967
 
0.2%
Dash Punctuation1646
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
59682
 
4.8%
48910
 
3.9%
43855
 
3.5%
37035
 
3.0%
34661
 
2.8%
32256
 
2.6%
29325
 
2.4%
22409
 
1.8%
21893
 
1.8%
21263
 
1.7%
Other values (680)893998
71.8%
ValueCountFrequency (%)
J3763
23.9%
Z2117
13.4%
A1789
11.3%
R1690
10.7%
M1233
 
7.8%
T1006
 
6.4%
P911
 
5.8%
S662
 
4.2%
F412
 
2.6%
C410
 
2.6%
Other values (19)1782
11.3%
ValueCountFrequency (%)
188243
100.0%
ValueCountFrequency (%)
.15741
100.0%
ValueCountFrequency (%)
r2967
100.0%
ValueCountFrequency (%)
-1646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1245287
84.7%
Common205630
 
14.0%
Latin18742
 
1.3%

Most frequent character per script

ValueCountFrequency (%)
59682
 
4.8%
48910
 
3.9%
43855
 
3.5%
37035
 
3.0%
34661
 
2.8%
32256
 
2.6%
29325
 
2.4%
22409
 
1.8%
21893
 
1.8%
21263
 
1.7%
Other values (680)893998
71.8%
ValueCountFrequency (%)
J3763
20.1%
r2967
15.8%
Z2117
11.3%
A1789
9.5%
R1690
9.0%
M1233
 
6.6%
T1006
 
5.4%
P911
 
4.9%
S662
 
3.5%
F412
 
2.2%
Other values (20)2192
11.7%
ValueCountFrequency (%)
188243
91.5%
.15741
 
7.7%
-1646
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul1245287
84.7%
ASCII224336
 
15.3%
None36
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
59682
 
4.8%
48910
 
3.9%
43855
 
3.5%
37035
 
3.0%
34661
 
2.8%
32256
 
2.6%
29325
 
2.4%
22409
 
1.8%
21893
 
1.8%
21263
 
1.7%
Other values (680)893998
71.8%
ValueCountFrequency (%)
188243
83.9%
.15741
 
7.0%
J3763
 
1.7%
r2967
 
1.3%
Z2117
 
0.9%
A1789
 
0.8%
R1690
 
0.8%
-1646
 
0.7%
M1233
 
0.5%
T1006
 
0.4%
Other values (19)4141
 
1.8%
ValueCountFrequency (%)
Ș29
80.6%
Ö3
 
8.3%
İ2
 
5.6%
Ł2
 
5.6%

선수7
Categorical

HIGH CARDINALITY
MISSING

Distinct5413
Distinct (%)2.4%
Missing2520
Missing (%)1.1%
Memory size1.8 MiB
이반 페리시치
 
6231
티보 쿠르투아
 
6121
제롬 보아텡
 
5916
사미 케디라
 
5227
호나우두
 
5151
Other values (5408)
200964 

Length

Max length13
Median length7
Mean length6.399795305
Min length1

Characters and Unicode

Total characters1469457
Distinct characters732
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1633 ?
Unique (%)0.7%

Sample

1st row정산
2nd row티보 쿠르투아
3rd row페페
4th row가레스 베일
5th row김신욱
ValueCountFrequency (%)
이반 페리시치6231
 
2.7%
티보 쿠르투아6121
 
2.6%
제롬 보아텡5916
 
2.5%
사미 케디라5227
 
2.3%
호나우두5151
 
2.2%
네이마르 Jr.4149
 
1.8%
카일 워커3699
 
1.6%
아르투로 비달3686
 
1.6%
가레스 베일3156
 
1.4%
크리스티아누 호날두3145
 
1.4%
Other values (5403)183129
78.9%
2021-01-27T18:10:45.046318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이반6844
 
1.6%
페리시치6231
 
1.5%
보아텡6199
 
1.5%
쿠르투아6121
 
1.5%
티보6121
 
1.5%
제롬5944
 
1.4%
케디라5230
 
1.2%
사미5228
 
1.2%
호나우두5160
 
1.2%
네이마르4149
 
1.0%
Other values (5952)362508
86.4%

Most occurring characters

ValueCountFrequency (%)
190125
 
12.9%
56476
 
3.8%
50491
 
3.4%
41797
 
2.8%
37975
 
2.6%
36723
 
2.5%
32422
 
2.2%
26367
 
1.8%
22595
 
1.5%
21810
 
1.5%
Other values (722)952676
64.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter1240873
84.4%
Space Separator190125
 
12.9%
Uppercase Letter16440
 
1.1%
Other Punctuation16396
 
1.1%
Lowercase Letter4149
 
0.3%
Dash Punctuation1474
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
56476
 
4.6%
50491
 
4.1%
41797
 
3.4%
37975
 
3.1%
36723
 
3.0%
32422
 
2.6%
26367
 
2.1%
22595
 
1.8%
21810
 
1.8%
20904
 
1.7%
Other values (689)893313
72.0%
ValueCountFrequency (%)
J5069
30.8%
R1759
 
10.7%
A1733
 
10.5%
Z1430
 
8.7%
M1266
 
7.7%
P1097
 
6.7%
T1003
 
6.1%
S432
 
2.6%
C418
 
2.5%
F319
 
1.9%
Other values (19)1914
 
11.6%
ValueCountFrequency (%)
190125
100.0%
ValueCountFrequency (%)
r4149
100.0%
ValueCountFrequency (%)
.16396
100.0%
ValueCountFrequency (%)
-1474
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1240873
84.4%
Common207995
 
14.2%
Latin20589
 
1.4%

Most frequent character per script

ValueCountFrequency (%)
56476
 
4.6%
50491
 
4.1%
41797
 
3.4%
37975
 
3.1%
36723
 
3.0%
32422
 
2.6%
26367
 
2.1%
22595
 
1.8%
21810
 
1.8%
20904
 
1.7%
Other values (689)893313
72.0%
ValueCountFrequency (%)
J5069
24.6%
r4149
20.2%
R1759
 
8.5%
A1733
 
8.4%
Z1430
 
6.9%
M1266
 
6.1%
P1097
 
5.3%
T1003
 
4.9%
S432
 
2.1%
C418
 
2.0%
Other values (20)2233
10.8%
ValueCountFrequency (%)
190125
91.4%
.16396
 
7.9%
-1474
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul1240873
84.4%
ASCII228557
 
15.6%
None27
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
56476
 
4.6%
50491
 
4.1%
41797
 
3.4%
37975
 
3.1%
36723
 
3.0%
32422
 
2.6%
26367
 
2.1%
22595
 
1.8%
21810
 
1.8%
20904
 
1.7%
Other values (689)893313
72.0%
ValueCountFrequency (%)
190125
83.2%
.16396
 
7.2%
J5069
 
2.2%
r4149
 
1.8%
R1759
 
0.8%
A1733
 
0.8%
-1474
 
0.6%
Z1430
 
0.6%
M1266
 
0.6%
P1097
 
0.5%
Other values (18)4059
 
1.8%
ValueCountFrequency (%)
Ș19
70.4%
Ł4
 
14.8%
Ö2
 
7.4%
İ1
 
3.7%
Á1
 
3.7%

선수8
Categorical

HIGH CARDINALITY
MISSING

Distinct5413
Distinct (%)2.4%
Missing2504
Missing (%)1.1%
Memory size1.8 MiB
티보 쿠르투아
 
8078
제롬 보아텡
 
5805
이반 페리시치
 
5427
네이마르 Jr.
 
4879
사미 케디라
 
4590
Other values (5408)
200847 

Length

Max length13
Median length7
Mean length6.390905211
Min length1

Characters and Unicode

Total characters1467518
Distinct characters728
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1553 ?
Unique (%)0.7%

Sample

1st row김신욱
2nd row마티아스 치머만
3rd row카림 벤제마
4th row이반 페리시치
5th row홍철
ValueCountFrequency (%)
티보 쿠르투아8078
 
3.5%
제롬 보아텡5805
 
2.5%
이반 페리시치5427
 
2.3%
네이마르 Jr.4879
 
2.1%
사미 케디라4590
 
2.0%
케빈 더브라위너4220
 
1.8%
라파엘 바란4031
 
1.7%
카일 워커3744
 
1.6%
호나우두3407
 
1.5%
아르투로 비달3318
 
1.4%
Other values (5403)182127
78.5%
2021-01-27T18:10:45.279997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
티보8080
 
1.9%
쿠르투아8078
 
1.9%
보아텡6144
 
1.5%
이반6016
 
1.4%
제롬5827
 
1.4%
페리시치5427
 
1.3%
jr4879
 
1.2%
네이마르4879
 
1.2%
케빈4774
 
1.1%
케디라4595
 
1.1%
Other values (5995)361350
86.0%

Most occurring characters

ValueCountFrequency (%)
190423
 
13.0%
53972
 
3.7%
51345
 
3.5%
39983
 
2.7%
38145
 
2.6%
37003
 
2.5%
34267
 
2.3%
25610
 
1.7%
22854
 
1.6%
21529
 
1.5%
Other values (718)952387
64.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter1234922
84.2%
Space Separator190423
 
13.0%
Uppercase Letter17884
 
1.2%
Other Punctuation17850
 
1.2%
Lowercase Letter4879
 
0.3%
Dash Punctuation1560
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
53972
 
4.4%
51345
 
4.2%
39983
 
3.2%
38145
 
3.1%
37003
 
3.0%
34267
 
2.8%
25610
 
2.1%
22854
 
1.9%
21529
 
1.7%
21003
 
1.7%
Other values (685)889211
72.0%
ValueCountFrequency (%)
J6267
35.0%
A2147
 
12.0%
R1815
 
10.1%
M1442
 
8.1%
P1205
 
6.7%
T995
 
5.6%
Z988
 
5.5%
S406
 
2.3%
C384
 
2.1%
F308
 
1.7%
Other values (19)1927
 
10.8%
ValueCountFrequency (%)
190423
100.0%
ValueCountFrequency (%)
.17850
100.0%
ValueCountFrequency (%)
r4879
100.0%
ValueCountFrequency (%)
-1560
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1234922
84.2%
Common209833
 
14.3%
Latin22763
 
1.6%

Most frequent character per script

ValueCountFrequency (%)
53972
 
4.4%
51345
 
4.2%
39983
 
3.2%
38145
 
3.1%
37003
 
3.0%
34267
 
2.8%
25610
 
2.1%
22854
 
1.9%
21529
 
1.7%
21003
 
1.7%
Other values (685)889211
72.0%
ValueCountFrequency (%)
J6267
27.5%
r4879
21.4%
A2147
 
9.4%
R1815
 
8.0%
M1442
 
6.3%
P1205
 
5.3%
T995
 
4.4%
Z988
 
4.3%
S406
 
1.8%
C384
 
1.7%
Other values (20)2235
 
9.8%
ValueCountFrequency (%)
190423
90.7%
.17850
 
8.5%
-1560
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul1234922
84.2%
ASCII232582
 
15.8%
None14
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
53972
 
4.4%
51345
 
4.2%
39983
 
3.2%
38145
 
3.1%
37003
 
3.0%
34267
 
2.8%
25610
 
2.1%
22854
 
1.9%
21529
 
1.7%
21003
 
1.7%
Other values (685)889211
72.0%
ValueCountFrequency (%)
190423
81.9%
.17850
 
7.7%
J6267
 
2.7%
r4879
 
2.1%
A2147
 
0.9%
R1815
 
0.8%
-1560
 
0.7%
M1442
 
0.6%
P1205
 
0.5%
T995
 
0.4%
Other values (18)3999
 
1.7%
ValueCountFrequency (%)
İ5
35.7%
Ș3
21.4%
Ö3
21.4%
Ł2
 
14.3%
Í1
 
7.1%

선수9
Categorical

HIGH CARDINALITY
MISSING

Distinct5626
Distinct (%)2.4%
Missing2484
Missing (%)1.1%
Memory size1.8 MiB
티보 쿠르투아
 
9627
라파엘 바란
 
5747
케빈 더브라위너
 
5149
네이마르 Jr.
 
4899
제롬 보아텡
 
4788
Other values (5621)
199436 

Length

Max length15
Median length7
Mean length6.377846773
Min length1

Characters and Unicode

Total characters1464647
Distinct characters744
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1724 ?
Unique (%)0.8%

Sample

1st row홍철
2nd row손흥민
3rd row아르투로 비달
4th row패트릭 반안홀트
5th row손흥민
ValueCountFrequency (%)
티보 쿠르투아9627
 
4.1%
라파엘 바란5747
 
2.5%
케빈 더브라위너5149
 
2.2%
네이마르 Jr.4899
 
2.1%
제롬 보아텡4788
 
2.1%
이반 페리시치3903
 
1.7%
카일 워커3869
 
1.7%
버질 반데이크3606
 
1.6%
루드 굴리트3468
 
1.5%
손흥민3328
 
1.4%
Other values (5616)181262
78.1%
2021-01-27T18:10:45.509580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
티보9630
 
2.3%
쿠르투아9627
 
2.3%
케빈5820
 
1.4%
라파엘5802
 
1.4%
바란5747
 
1.4%
더브라위너5149
 
1.2%
보아텡5136
 
1.2%
jr4899
 
1.2%
네이마르4899
 
1.2%
제롬4833
 
1.2%
Other values (6165)358071
85.3%

Most occurring characters

ValueCountFrequency (%)
189967
 
13.0%
52956
 
3.6%
51883
 
3.5%
38714
 
2.6%
36634
 
2.5%
35936
 
2.5%
35674
 
2.4%
24305
 
1.7%
23284
 
1.6%
21175
 
1.4%
Other values (734)954119
65.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter1233963
84.2%
Space Separator189967
 
13.0%
Uppercase Letter17135
 
1.2%
Other Punctuation17111
 
1.2%
Lowercase Letter4899
 
0.3%
Dash Punctuation1572
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
52956
 
4.3%
51883
 
4.2%
38714
 
3.1%
36634
 
3.0%
35936
 
2.9%
35674
 
2.9%
24305
 
2.0%
23284
 
1.9%
21175
 
1.7%
20909
 
1.7%
Other values (699)892493
72.3%
ValueCountFrequency (%)
J6402
37.4%
A2010
 
11.7%
P1329
 
7.8%
R1274
 
7.4%
M1239
 
7.2%
T841
 
4.9%
Z799
 
4.7%
C590
 
3.4%
S401
 
2.3%
F344
 
2.0%
Other values (21)1906
 
11.1%
ValueCountFrequency (%)
189967
100.0%
ValueCountFrequency (%)
r4899
100.0%
ValueCountFrequency (%)
.17111
100.0%
ValueCountFrequency (%)
-1572
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1233963
84.2%
Common208650
 
14.2%
Latin22034
 
1.5%

Most frequent character per script

ValueCountFrequency (%)
52956
 
4.3%
51883
 
4.2%
38714
 
3.1%
36634
 
3.0%
35936
 
2.9%
35674
 
2.9%
24305
 
2.0%
23284
 
1.9%
21175
 
1.7%
20909
 
1.7%
Other values (699)892493
72.3%
ValueCountFrequency (%)
J6402
29.1%
r4899
22.2%
A2010
 
9.1%
P1329
 
6.0%
R1274
 
5.8%
M1239
 
5.6%
T841
 
3.8%
Z799
 
3.6%
C590
 
2.7%
S401
 
1.8%
Other values (22)2250
 
10.2%
ValueCountFrequency (%)
189967
91.0%
.17111
 
8.2%
-1572
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul1233963
84.2%
ASCII230664
 
15.7%
None20
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
52956
 
4.3%
51883
 
4.2%
38714
 
3.1%
36634
 
3.0%
35936
 
2.9%
35674
 
2.9%
24305
 
2.0%
23284
 
1.9%
21175
 
1.7%
20909
 
1.7%
Other values (699)892493
72.3%
ValueCountFrequency (%)
189967
82.4%
.17111
 
7.4%
J6402
 
2.8%
r4899
 
2.1%
A2010
 
0.9%
-1572
 
0.7%
P1329
 
0.6%
R1274
 
0.6%
M1239
 
0.5%
T841
 
0.4%
Other values (18)4020
 
1.7%
ValueCountFrequency (%)
Ł10
50.0%
Ș2
 
10.0%
Ö2
 
10.0%
İ2
 
10.0%
Á2
 
10.0%
É1
 
5.0%
Ó1
 
5.0%

선수10
Categorical

HIGH CARDINALITY
MISSING

Distinct5680
Distinct (%)2.5%
Missing2477
Missing (%)1.1%
Memory size1.8 MiB
티보 쿠르투아
 
10042
라파엘 바란
 
7394
케빈 더브라위너
 
5735
버질 반데이크
 
4427
네이마르 Jr.
 
4410
Other values (5675)
197645 

Length

Max length13
Median length7
Mean length6.348299391
Min length1

Characters and Unicode

Total characters1457906
Distinct characters742
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1683 ?
Unique (%)0.7%

Sample

1st row손흥민
2nd row토마시 쿠베크
3rd row제롬 보아텡
4th row파울리뉴
5th row신세계
ValueCountFrequency (%)
티보 쿠르투아10042
 
4.3%
라파엘 바란7394
 
3.2%
케빈 더브라위너5735
 
2.5%
버질 반데이크4427
 
1.9%
네이마르 Jr.4410
 
1.9%
루드 굴리트4358
 
1.9%
손흥민4329
 
1.9%
카일 워커3220
 
1.4%
제롬 보아텡3211
 
1.4%
폴 포그바2897
 
1.2%
Other values (5670)179630
77.4%
2021-01-27T18:10:45.733895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
쿠르투아10042
 
2.4%
티보10042
 
2.4%
라파엘7462
 
1.8%
바란7394
 
1.8%
케빈6511
 
1.6%
더브라위너5735
 
1.4%
버질4427
 
1.1%
반데이크4427
 
1.1%
네이마르4410
 
1.1%
jr4410
 
1.1%
Other values (6237)353730
84.5%

Most occurring characters

ValueCountFrequency (%)
188937
 
13.0%
52193
 
3.6%
49414
 
3.4%
39213
 
2.7%
37028
 
2.5%
35584
 
2.4%
32430
 
2.2%
24866
 
1.7%
22550
 
1.5%
20795
 
1.4%
Other values (732)954896
65.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter1228964
84.3%
Space Separator188937
 
13.0%
Uppercase Letter16969
 
1.2%
Other Punctuation16903
 
1.2%
Lowercase Letter4410
 
0.3%
Dash Punctuation1723
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
52193
 
4.2%
49414
 
4.0%
39213
 
3.2%
37028
 
3.0%
35584
 
2.9%
32430
 
2.6%
24866
 
2.0%
22550
 
1.8%
20795
 
1.7%
20370
 
1.7%
Other values (698)894521
72.8%
ValueCountFrequency (%)
J6232
36.7%
A2203
 
13.0%
P1479
 
8.7%
M1465
 
8.6%
R976
 
5.8%
T693
 
4.1%
Z629
 
3.7%
C509
 
3.0%
S443
 
2.6%
F386
 
2.3%
Other values (20)1954
 
11.5%
ValueCountFrequency (%)
188937
100.0%
ValueCountFrequency (%)
-1723
100.0%
ValueCountFrequency (%)
r4410
100.0%
ValueCountFrequency (%)
.16903
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1228964
84.3%
Common207563
 
14.2%
Latin21379
 
1.5%

Most frequent character per script

ValueCountFrequency (%)
52193
 
4.2%
49414
 
4.0%
39213
 
3.2%
37028
 
3.0%
35584
 
2.9%
32430
 
2.6%
24866
 
2.0%
22550
 
1.8%
20795
 
1.7%
20370
 
1.7%
Other values (698)894521
72.8%
ValueCountFrequency (%)
J6232
29.2%
r4410
20.6%
A2203
 
10.3%
P1479
 
6.9%
M1465
 
6.9%
R976
 
4.6%
T693
 
3.2%
Z629
 
2.9%
C509
 
2.4%
S443
 
2.1%
Other values (21)2340
 
10.9%
ValueCountFrequency (%)
188937
91.0%
.16903
 
8.1%
-1723
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul1228964
84.3%
ASCII228919
 
15.7%
None23
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
52193
 
4.2%
49414
 
4.0%
39213
 
3.2%
37028
 
3.0%
35584
 
2.9%
32430
 
2.6%
24866
 
2.0%
22550
 
1.8%
20795
 
1.7%
20370
 
1.7%
Other values (698)894521
72.8%
ValueCountFrequency (%)
188937
82.5%
.16903
 
7.4%
J6232
 
2.7%
r4410
 
1.9%
A2203
 
1.0%
-1723
 
0.8%
P1479
 
0.6%
M1465
 
0.6%
R976
 
0.4%
T693
 
0.3%
Other values (19)3898
 
1.7%
ValueCountFrequency (%)
Ö11
47.8%
Ł5
21.7%
İ4
 
17.4%
Ș2
 
8.7%
Ó1
 
4.3%

선수11
Categorical

HIGH CARDINALITY
MISSING

Distinct5702
Distinct (%)2.5%
Missing2564
Missing (%)1.1%
Memory size1.8 MiB
라파엘 바란
 
9722
티보 쿠르투아
 
8525
루드 굴리트
 
6511
케빈 더브라위너
 
5405
버질 반데이크
 
5201
Other values (5697)
194202 

Length

Max length13
Median length7
Mean length6.302627567
Min length1

Characters and Unicode

Total characters1446869
Distinct characters739
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1618 ?
Unique (%)0.7%

Sample

1st row신세계
2nd row마우로 이카르디
3rd row차범근
4th row루카시 흐라데키
5th row고광민
ValueCountFrequency (%)
라파엘 바란9722
 
4.2%
티보 쿠르투아8525
 
3.7%
루드 굴리트6511
 
2.8%
케빈 더브라위너5405
 
2.3%
버질 반데이크5201
 
2.2%
손흥민4875
 
2.1%
네이마르 Jr.4014
 
1.7%
세르주 그나브리3471
 
1.5%
폴 포그바2886
 
1.2%
카일 워커2728
 
1.2%
Other values (5692)176228
75.9%
(Missing)2564
 
1.1%
2021-01-27T18:10:45.955646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
라파엘9800
 
2.3%
바란9722
 
2.3%
티보8525
 
2.0%
쿠르투아8525
 
2.0%
루드6516
 
1.6%
굴리트6511
 
1.6%
케빈6306
 
1.5%
더브라위너5405
 
1.3%
반데이크5202
 
1.2%
버질5201
 
1.2%
Other values (6238)345917
82.8%

Most occurring characters

ValueCountFrequency (%)
188064
 
13.0%
51439
 
3.6%
47628
 
3.3%
41012
 
2.8%
37601
 
2.6%
34168
 
2.4%
28759
 
2.0%
26605
 
1.8%
22326
 
1.5%
21286
 
1.5%
Other values (729)947981
65.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter1220121
84.3%
Space Separator188064
 
13.0%
Uppercase Letter16546
 
1.1%
Other Punctuation16426
 
1.1%
Lowercase Letter4014
 
0.3%
Dash Punctuation1698
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
51439
 
4.2%
47628
 
3.9%
41012
 
3.4%
37601
 
3.1%
34168
 
2.8%
28759
 
2.4%
26605
 
2.2%
22326
 
1.8%
21286
 
1.7%
21268
 
1.7%
Other values (695)888029
72.8%
ValueCountFrequency (%)
J5997
36.2%
A1973
 
11.9%
P1439
 
8.7%
M1422
 
8.6%
R870
 
5.3%
T595
 
3.6%
C569
 
3.4%
Z553
 
3.3%
S522
 
3.2%
F469
 
2.8%
Other values (20)2137
 
12.9%
ValueCountFrequency (%)
188064
100.0%
ValueCountFrequency (%)
.16426
100.0%
ValueCountFrequency (%)
-1698
100.0%
ValueCountFrequency (%)
r4014
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1220121
84.3%
Common206188
 
14.3%
Latin20560
 
1.4%

Most frequent character per script

ValueCountFrequency (%)
51439
 
4.2%
47628
 
3.9%
41012
 
3.4%
37601
 
3.1%
34168
 
2.8%
28759
 
2.4%
26605
 
2.2%
22326
 
1.8%
21286
 
1.7%
21268
 
1.7%
Other values (695)888029
72.8%
ValueCountFrequency (%)
J5997
29.2%
r4014
19.5%
A1973
 
9.6%
P1439
 
7.0%
M1422
 
6.9%
R870
 
4.2%
T595
 
2.9%
C569
 
2.8%
Z553
 
2.7%
S522
 
2.5%
Other values (21)2606
12.7%
ValueCountFrequency (%)
188064
91.2%
.16426
 
8.0%
-1698
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul1220121
84.3%
ASCII226719
 
15.7%
None29
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
51439
 
4.2%
47628
 
3.9%
41012
 
3.4%
37601
 
3.1%
34168
 
2.8%
28759
 
2.4%
26605
 
2.2%
22326
 
1.8%
21286
 
1.7%
21268
 
1.7%
Other values (695)888029
72.8%
ValueCountFrequency (%)
188064
83.0%
.16426
 
7.2%
J5997
 
2.6%
r4014
 
1.8%
A1973
 
0.9%
-1698
 
0.7%
P1439
 
0.6%
M1422
 
0.6%
R870
 
0.4%
T595
 
0.3%
Other values (19)4221
 
1.9%
ValueCountFrequency (%)
Ö10
34.5%
Ł10
34.5%
Ș7
24.1%
Á1
 
3.4%
İ1
 
3.4%

선수12
Categorical

HIGH CARDINALITY
MISSING

Distinct5859
Distinct (%)2.6%
Missing2549
Missing (%)1.1%
Memory size1.8 MiB
라파엘 바란
 
10181
루드 굴리트
 
9632
티보 쿠르투아
 
6793
버질 반데이크
 
5544
케빈 더브라위너
 
4992
Other values (5854)
192439 

Length

Max length13
Median length7
Mean length6.291971025
Min length1

Characters and Unicode

Total characters1444517
Distinct characters745
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1664 ?
Unique (%)0.7%

Sample

1st row알레시오 크라뇨
2nd row레뱅 퀴르자와
3rd row티보 쿠르투아
4th row조엘 마티프
5th row케빈 호드리게스
ValueCountFrequency (%)
라파엘 바란10181
 
4.4%
루드 굴리트9632
 
4.1%
티보 쿠르투아6793
 
2.9%
버질 반데이크5544
 
2.4%
케빈 더브라위너4992
 
2.2%
손흥민4950
 
2.1%
세르주 그나브리3983
 
1.7%
프랑크 레이카르트3359
 
1.4%
폴 포그바2943
 
1.3%
네이마르 Jr.2762
 
1.2%
Other values (5849)174442
75.1%
(Missing)2549
 
1.1%
2021-01-27T18:10:46.176261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
라파엘10228
 
2.4%
바란10181
 
2.4%
루드9633
 
2.3%
굴리트9632
 
2.3%
티보6794
 
1.6%
쿠르투아6793
 
1.6%
케빈6027
 
1.4%
버질5544
 
1.3%
반데이크5544
 
1.3%
더브라위너4992
 
1.2%
Other values (6352)342529
82.0%

Most occurring characters

ValueCountFrequency (%)
188316
 
13.0%
49323
 
3.4%
46832
 
3.2%
44629
 
3.1%
35839
 
2.5%
33309
 
2.3%
28675
 
2.0%
26231
 
1.8%
24486
 
1.7%
23694
 
1.6%
Other values (735)943183
65.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter1220626
84.5%
Space Separator188316
 
13.0%
Uppercase Letter15311
 
1.1%
Other Punctuation15259
 
1.1%
Lowercase Letter2762
 
0.2%
Dash Punctuation2243
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
49323
 
4.0%
46832
 
3.8%
44629
 
3.7%
35839
 
2.9%
33309
 
2.7%
28675
 
2.3%
26231
 
2.1%
24486
 
2.0%
23694
 
1.9%
22508
 
1.8%
Other values (702)885100
72.5%
ValueCountFrequency (%)
J4307
28.1%
A1854
12.1%
M1563
 
10.2%
P1231
 
8.0%
R842
 
5.5%
S695
 
4.5%
T647
 
4.2%
C627
 
4.1%
Z507
 
3.3%
D481
 
3.1%
Other values (19)2557
16.7%
ValueCountFrequency (%)
188316
100.0%
ValueCountFrequency (%)
.15259
100.0%
ValueCountFrequency (%)
-2243
100.0%
ValueCountFrequency (%)
r2762
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1220626
84.5%
Common205818
 
14.2%
Latin18073
 
1.3%

Most frequent character per script

ValueCountFrequency (%)
49323
 
4.0%
46832
 
3.8%
44629
 
3.7%
35839
 
2.9%
33309
 
2.7%
28675
 
2.3%
26231
 
2.1%
24486
 
2.0%
23694
 
1.9%
22508
 
1.8%
Other values (702)885100
72.5%
ValueCountFrequency (%)
J4307
23.8%
r2762
15.3%
A1854
10.3%
M1563
 
8.6%
P1231
 
6.8%
R842
 
4.7%
S695
 
3.8%
T647
 
3.6%
C627
 
3.5%
Z507
 
2.8%
Other values (20)3038
16.8%
ValueCountFrequency (%)
188316
91.5%
.15259
 
7.4%
-2243
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul1220626
84.5%
ASCII223824
 
15.5%
None67
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
49323
 
4.0%
46832
 
3.8%
44629
 
3.7%
35839
 
2.9%
33309
 
2.7%
28675
 
2.3%
26231
 
2.1%
24486
 
2.0%
23694
 
1.9%
22508
 
1.8%
Other values (702)885100
72.5%
ValueCountFrequency (%)
188316
84.1%
.15259
 
6.8%
J4307
 
1.9%
r2762
 
1.2%
-2243
 
1.0%
A1854
 
0.8%
M1563
 
0.7%
P1231
 
0.5%
R842
 
0.4%
S695
 
0.3%
Other values (19)4752
 
2.1%
ValueCountFrequency (%)
Ö41
61.2%
Ł20
29.9%
Ș3
 
4.5%
Á3
 
4.5%

선수13
Categorical

HIGH CARDINALITY
MISSING

Distinct5956
Distinct (%)2.6%
Missing2505
Missing (%)1.1%
Memory size1.8 MiB
루드 굴리트
 
14445
라파엘 바란
 
10017
버질 반데이크
 
4956
티보 쿠르투아
 
4931
프랑크 레이카르트
 
4205
Other values (5951)
191071 

Length

Max length13
Median length7
Mean length6.31760479
Min length1

Characters and Unicode

Total characters1450680
Distinct characters743
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1671 ?
Unique (%)0.7%

Sample

1st row고광민
2nd row라파엘 바란
3rd row손흥민
4th row토마시 홀리
5th row권용현
ValueCountFrequency (%)
루드 굴리트14445
 
6.2%
라파엘 바란10017
 
4.3%
버질 반데이크4956
 
2.1%
티보 쿠르투아4931
 
2.1%
프랑크 레이카르트4205
 
1.8%
세르주 그나브리4153
 
1.8%
케빈 더브라위너3980
 
1.7%
손흥민3909
 
1.7%
요주아 키미히2279
 
1.0%
테오 에르난데스2253
 
1.0%
Other values (5946)174497
75.2%
(Missing)2505
 
1.1%
2021-01-27T18:10:46.404708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
루드14448
 
3.4%
굴리트14445
 
3.4%
라파엘10083
 
2.4%
바란10017
 
2.4%
반데이크4956
 
1.2%
버질4956
 
1.2%
티보4931
 
1.2%
쿠르투아4931
 
1.2%
케빈4830
 
1.1%
프랑크4482
 
1.1%
Other values (6430)342484
81.4%

Most occurring characters

ValueCountFrequency (%)
190938
 
13.2%
50038
 
3.4%
48633
 
3.4%
45207
 
3.1%
33645
 
2.3%
32182
 
2.2%
32097
 
2.2%
29199
 
2.0%
28735
 
2.0%
23349
 
1.6%
Other values (733)936657
64.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter1225797
84.5%
Space Separator190938
 
13.2%
Uppercase Letter14882
 
1.0%
Other Punctuation14628
 
1.0%
Dash Punctuation2290
 
0.2%
Lowercase Letter2145
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
50038
 
4.1%
48633
 
4.0%
45207
 
3.7%
33645
 
2.7%
32182
 
2.6%
32097
 
2.6%
29199
 
2.4%
28735
 
2.3%
23349
 
1.9%
22037
 
1.8%
Other values (700)880675
71.8%
ValueCountFrequency (%)
J3611
24.3%
A1843
12.4%
M1571
10.6%
P1510
10.1%
R918
 
6.2%
C742
 
5.0%
T669
 
4.5%
S630
 
4.2%
F541
 
3.6%
D482
 
3.2%
Other values (19)2365
15.9%
ValueCountFrequency (%)
190938
100.0%
ValueCountFrequency (%)
.14628
100.0%
ValueCountFrequency (%)
-2290
100.0%
ValueCountFrequency (%)
r2145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1225797
84.5%
Common207856
 
14.3%
Latin17027
 
1.2%

Most frequent character per script

ValueCountFrequency (%)
50038
 
4.1%
48633
 
4.0%
45207
 
3.7%
33645
 
2.7%
32182
 
2.6%
32097
 
2.6%
29199
 
2.4%
28735
 
2.3%
23349
 
1.9%
22037
 
1.8%
Other values (700)880675
71.8%
ValueCountFrequency (%)
J3611
21.2%
r2145
12.6%
A1843
10.8%
M1571
9.2%
P1510
8.9%
R918
 
5.4%
C742
 
4.4%
T669
 
3.9%
S630
 
3.7%
F541
 
3.2%
Other values (20)2847
16.7%
ValueCountFrequency (%)
190938
91.9%
.14628
 
7.0%
-2290
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul1225797
84.5%
ASCII224864
 
15.5%
None19
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
50038
 
4.1%
48633
 
4.0%
45207
 
3.7%
33645
 
2.7%
32182
 
2.6%
32097
 
2.6%
29199
 
2.4%
28735
 
2.3%
23349
 
1.9%
22037
 
1.8%
Other values (700)880675
71.8%
ValueCountFrequency (%)
190938
84.9%
.14628
 
6.5%
J3611
 
1.6%
-2290
 
1.0%
r2145
 
1.0%
A1843
 
0.8%
M1571
 
0.7%
P1510
 
0.7%
R918
 
0.4%
C742
 
0.3%
Other values (18)4668
 
2.1%
ValueCountFrequency (%)
İ7
36.8%
Ö5
26.3%
Ł3
15.8%
Ș3
15.8%
Á1
 
5.3%

선수14
Categorical

HIGH CARDINALITY
MISSING

Distinct6080
Distinct (%)2.6%
Missing2507
Missing (%)1.1%
Memory size1.8 MiB
루드 굴리트
 
17551
라파엘 바란
 
8096
프랑크 레이카르트
 
5366
버질 반데이크
 
4164
테오 에르난데스
 
4101
Other values (6075)
190345 

Length

Max length13
Median length7
Mean length6.378746903
Min length1

Characters and Unicode

Total characters1464707
Distinct characters761
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1713 ?
Unique (%)0.7%

Sample

1st row권용현
2nd row루드 굴리트
3rd row프랑크 레이카르트
4th row버질 반데이크
5th row페드루 엔히키
ValueCountFrequency (%)
루드 굴리트17551
 
7.6%
라파엘 바란8096
 
3.5%
프랑크 레이카르트5366
 
2.3%
버질 반데이크4164
 
1.8%
테오 에르난데스4101
 
1.8%
세르주 그나브리3428
 
1.5%
티보 쿠르투아3426
 
1.5%
손흥민3183
 
1.4%
케빈 더브라위너2706
 
1.2%
주앙 칸셀루2604
 
1.1%
Other values (6070)174998
75.4%
(Missing)2507
 
1.1%
2021-01-27T18:10:46.637890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
굴리트17553
 
4.2%
루드17551
 
4.2%
라파엘8129
 
1.9%
바란8096
 
1.9%
프랑크5618
 
1.3%
레이카르트5366
 
1.3%
에르난데스4944
 
1.2%
버질4164
 
1.0%
반데이크4164
 
1.0%
테오4111
 
1.0%
Other values (6590)343126
81.2%

Most occurring characters

ValueCountFrequency (%)
193199
 
13.2%
53530
 
3.7%
49599
 
3.4%
47370
 
3.2%
34593
 
2.4%
32940
 
2.2%
32492
 
2.2%
31903
 
2.2%
29916
 
2.0%
22288
 
1.5%
Other values (751)936877
64.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter1238256
84.5%
Space Separator193199
 
13.2%
Uppercase Letter14283
 
1.0%
Other Punctuation14103
 
1.0%
Dash Punctuation3169
 
0.2%
Lowercase Letter1696
 
0.1%
Decimal Number1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
53530
 
4.3%
49599
 
4.0%
47370
 
3.8%
34593
 
2.8%
32940
 
2.7%
32492
 
2.6%
31903
 
2.6%
29916
 
2.4%
22288
 
1.8%
21929
 
1.8%
Other values (713)881696
71.2%
ValueCountFrequency (%)
J2778
19.4%
A1856
13.0%
M1461
10.2%
R1114
7.8%
T1063
 
7.4%
P1032
 
7.2%
C807
 
5.7%
S630
 
4.4%
D477
 
3.3%
Z435
 
3.0%
Other values (23)2630
18.4%
ValueCountFrequency (%)
193199
100.0%
ValueCountFrequency (%)
.14103
100.0%
ValueCountFrequency (%)
r1696
100.0%
ValueCountFrequency (%)
-3169
100.0%
ValueCountFrequency (%)
41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1238256
84.5%
Common210472
 
14.4%
Latin15979
 
1.1%

Most frequent character per script

ValueCountFrequency (%)
53530
 
4.3%
49599
 
4.0%
47370
 
3.8%
34593
 
2.8%
32940
 
2.7%
32492
 
2.6%
31903
 
2.6%
29916
 
2.4%
22288
 
1.8%
21929
 
1.8%
Other values (713)881696
71.2%
ValueCountFrequency (%)
J2778
17.4%
A1856
11.6%
r1696
10.6%
M1461
9.1%
R1114
 
7.0%
T1063
 
6.7%
P1032
 
6.5%
C807
 
5.1%
S630
 
3.9%
D477
 
3.0%
Other values (24)3065
19.2%
ValueCountFrequency (%)
193199
91.8%
.14103
 
6.7%
-3169
 
1.5%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul1238256
84.5%
ASCII226419
 
15.5%
None32
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
53530
 
4.3%
49599
 
4.0%
47370
 
3.8%
34593
 
2.8%
32940
 
2.7%
32492
 
2.6%
31903
 
2.6%
29916
 
2.4%
22288
 
1.8%
21929
 
1.8%
Other values (713)881696
71.2%
ValueCountFrequency (%)
193199
85.3%
.14103
 
6.2%
-3169
 
1.4%
J2778
 
1.2%
A1856
 
0.8%
r1696
 
0.7%
M1461
 
0.6%
R1114
 
0.5%
T1063
 
0.5%
P1032
 
0.5%
Other values (20)4948
 
2.2%
ValueCountFrequency (%)
İ12
37.5%
Ö8
25.0%
Ș5
15.6%
Ł2
 
6.2%
Ó2
 
6.2%
Ü1
 
3.1%
Ç1
 
3.1%
Á1
 
3.1%

선수15
Categorical

HIGH CARDINALITY
MISSING

Distinct6141
Distinct (%)2.7%
Missing2578
Missing (%)1.1%
Memory size1.8 MiB
루드 굴리트
19270 
테오 에르난데스
 
7300
라파엘 바란
 
5297
프랑크 레이카르트
 
4501
로랑 블랑
 
3555
Other values (6136)
189629 

Length

Max length13
Median length7
Mean length6.455535129
Min length1

Characters and Unicode

Total characters1481881
Distinct characters750
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1726 ?
Unique (%)0.8%

Sample

1st row알렉스
2nd row폴 베르나르도니
3rd row루드 굴리트
4th row세르주 그나브리
5th rowA. 사나브리아
ValueCountFrequency (%)
루드 굴리트19270
 
8.3%
테오 에르난데스7300
 
3.1%
라파엘 바란5297
 
2.3%
프랑크 레이카르트4501
 
1.9%
로랑 블랑3555
 
1.5%
잔루이지 돈나룸마2847
 
1.2%
버질 반데이크2840
 
1.2%
세르주 그나브리2526
 
1.1%
티보 쿠르투아2435
 
1.0%
주앙 칸셀루2292
 
1.0%
Other values (6131)176689
76.1%
(Missing)2578
 
1.1%
2021-01-27T18:10:46.876274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
루드19270
 
4.5%
굴리트19270
 
4.5%
에르난데스8039
 
1.9%
테오7311
 
1.7%
라파엘5325
 
1.3%
바란5297
 
1.2%
프랑크4720
 
1.1%
레이카르트4501
 
1.1%
로랑3612
 
0.9%
블랑3597
 
0.8%
Other values (6629)343531
80.9%

Most occurring characters

ValueCountFrequency (%)
194921
 
13.2%
56985
 
3.8%
50496
 
3.4%
50048
 
3.4%
36002
 
2.4%
34224
 
2.3%
33253
 
2.2%
31570
 
2.1%
26167
 
1.8%
25378
 
1.7%
Other values (740)942837
63.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter1252614
84.5%
Space Separator194921
 
13.2%
Uppercase Letter14323
 
1.0%
Other Punctuation14179
 
1.0%
Dash Punctuation4615
 
0.3%
Lowercase Letter1229
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
56985
 
4.5%
50496
 
4.0%
50048
 
4.0%
36002
 
2.9%
34224
 
2.7%
33253
 
2.7%
31570
 
2.5%
26167
 
2.1%
25378
 
2.0%
21508
 
1.7%
Other values (707)886983
70.8%
ValueCountFrequency (%)
J2356
16.4%
A1663
11.6%
T1301
9.1%
M1262
8.8%
R1160
8.1%
S922
 
6.4%
C904
 
6.3%
P826
 
5.8%
F569
 
4.0%
D543
 
3.8%
Other values (19)2817
19.7%
ValueCountFrequency (%)
194921
100.0%
ValueCountFrequency (%)
.14179
100.0%
ValueCountFrequency (%)
-4615
100.0%
ValueCountFrequency (%)
r1229
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1252614
84.5%
Common213715
 
14.4%
Latin15552
 
1.0%

Most frequent character per script

ValueCountFrequency (%)
56985
 
4.5%
50496
 
4.0%
50048
 
4.0%
36002
 
2.9%
34224
 
2.7%
33253
 
2.7%
31570
 
2.5%
26167
 
2.1%
25378
 
2.0%
21508
 
1.7%
Other values (707)886983
70.8%
ValueCountFrequency (%)
J2356
15.1%
A1663
10.7%
T1301
 
8.4%
M1262
 
8.1%
r1229
 
7.9%
R1160
 
7.5%
S922
 
5.9%
C904
 
5.8%
P826
 
5.3%
F569
 
3.7%
Other values (20)3360
21.6%
ValueCountFrequency (%)
194921
91.2%
.14179
 
6.6%
-4615
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul1252614
84.5%
ASCII229245
 
15.5%
None22
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
56985
 
4.5%
50496
 
4.0%
50048
 
4.0%
36002
 
2.9%
34224
 
2.7%
33253
 
2.7%
31570
 
2.5%
26167
 
2.1%
25378
 
2.0%
21508
 
1.7%
Other values (707)886983
70.8%
ValueCountFrequency (%)
194921
85.0%
.14179
 
6.2%
-4615
 
2.0%
J2356
 
1.0%
A1663
 
0.7%
T1301
 
0.6%
M1262
 
0.6%
r1229
 
0.5%
R1160
 
0.5%
S922
 
0.4%
Other values (19)5637
 
2.5%
ValueCountFrequency (%)
İ14
63.6%
Ł4
 
18.2%
Ö3
 
13.6%
Ș1
 
4.5%

선수16
Categorical

HIGH CARDINALITY
MISSING

Distinct6221
Distinct (%)2.7%
Missing2552
Missing (%)1.1%
Memory size1.8 MiB
루드 굴리트
 
16192
테오 에르난데스
 
12366
알폰소 데이비스
 
3905
잔루이지 돈나룸마
 
3640
라파엘 바란
 
3300
Other values (6216)
190175 

Length

Max length13
Median length7
Mean length6.523399455
Min length1

Characters and Unicode

Total characters1497629
Distinct characters752
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1805 ?
Unique (%)0.8%

Sample

1st row폴 베르나르도니
2nd row테오 에르난데스
3rd row미카엘 라우드루프
4th row루드 굴리트
5th row폴 베르나르도니
ValueCountFrequency (%)
루드 굴리트16192
 
7.0%
테오 에르난데스12366
 
5.3%
알폰소 데이비스3905
 
1.7%
잔루이지 돈나룸마3640
 
1.6%
라파엘 바란3300
 
1.4%
로랑 블랑3276
 
1.4%
아슈라프 하키미3119
 
1.3%
에데르 밀리탕2842
 
1.2%
프랑크 레이카르트2733
 
1.2%
피카요 토모리2622
 
1.1%
Other values (6211)175583
75.6%
2021-01-27T18:10:47.111851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
굴리트16192
 
3.8%
루드16192
 
3.8%
에르난데스13092
 
3.1%
테오12384
 
2.9%
데이비스4022
 
0.9%
알폰소3933
 
0.9%
멘디3885
 
0.9%
잔루이지3663
 
0.9%
돈나룸마3642
 
0.9%
로랑3328
 
0.8%
Other values (6707)344733
81.1%

Most occurring characters

ValueCountFrequency (%)
195488
 
13.1%
60749
 
4.1%
57329
 
3.8%
52574
 
3.5%
34034
 
2.3%
32315
 
2.2%
31476
 
2.1%
29873
 
2.0%
27844
 
1.9%
27436
 
1.8%
Other values (742)948511
63.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter1267438
84.6%
Space Separator195488
 
13.1%
Uppercase Letter14311
 
1.0%
Other Punctuation14125
 
0.9%
Dash Punctuation5307
 
0.4%
Lowercase Letter960
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
60749
 
4.8%
57329
 
4.5%
52574
 
4.1%
34034
 
2.7%
32315
 
2.5%
31476
 
2.5%
29873
 
2.4%
27844
 
2.2%
27436
 
2.2%
24017
 
1.9%
Other values (706)889791
70.2%
ValueCountFrequency (%)
J2045
14.3%
A1849
12.9%
T1797
12.6%
R1267
8.9%
M1133
 
7.9%
C928
 
6.5%
P719
 
5.0%
S591
 
4.1%
F560
 
3.9%
D469
 
3.3%
Other values (22)2953
20.6%
ValueCountFrequency (%)
195488
100.0%
ValueCountFrequency (%)
.14125
100.0%
ValueCountFrequency (%)
-5307
100.0%
ValueCountFrequency (%)
r960
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1267438
84.6%
Common214920
 
14.4%
Latin15271
 
1.0%

Most frequent character per script

ValueCountFrequency (%)
60749
 
4.8%
57329
 
4.5%
52574
 
4.1%
34034
 
2.7%
32315
 
2.5%
31476
 
2.5%
29873
 
2.4%
27844
 
2.2%
27436
 
2.2%
24017
 
1.9%
Other values (706)889791
70.2%
ValueCountFrequency (%)
J2045
13.4%
A1849
12.1%
T1797
11.8%
R1267
 
8.3%
M1133
 
7.4%
r960
 
6.3%
C928
 
6.1%
P719
 
4.7%
S591
 
3.9%
F560
 
3.7%
Other values (23)3422
22.4%
ValueCountFrequency (%)
195488
91.0%
.14125
 
6.6%
-5307
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul1267438
84.6%
ASCII230168
 
15.4%
None23
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
60749
 
4.8%
57329
 
4.5%
52574
 
4.1%
34034
 
2.7%
32315
 
2.5%
31476
 
2.5%
29873
 
2.4%
27844
 
2.2%
27436
 
2.2%
24017
 
1.9%
Other values (706)889791
70.2%
ValueCountFrequency (%)
195488
84.9%
.14125
 
6.1%
-5307
 
2.3%
J2045
 
0.9%
A1849
 
0.8%
T1797
 
0.8%
R1267
 
0.6%
M1133
 
0.5%
r960
 
0.4%
C928
 
0.4%
Other values (19)5269
 
2.3%
ValueCountFrequency (%)
Ö6
26.1%
Ü6
26.1%
İ3
13.0%
Á3
13.0%
Ł2
 
8.7%
Í2
 
8.7%
Ó1
 
4.3%

선수17
Categorical

HIGH CARDINALITY
MISSING

Distinct6462
Distinct (%)2.8%
Missing2575
Missing (%)1.1%
Memory size1.8 MiB
테오 에르난데스
 
14349
루드 굴리트
 
10044
에데르 밀리탕
 
6528
알폰소 데이비스
 
5908
아슈라프 하키미
 
5639
Other values (6457)
187087 

Length

Max length15
Median length7
Mean length6.505051077
Min length1

Characters and Unicode

Total characters1493267
Distinct characters759
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1955 ?
Unique (%)0.9%

Sample

1st row에밀 아우데로
2nd row이르지 파블렌카
3rd row페를랑 멘디
4th row로드리
5th row에밀 아우데로
ValueCountFrequency (%)
테오 에르난데스14349
 
6.2%
루드 굴리트10044
 
4.3%
에데르 밀리탕6528
 
2.8%
알폰소 데이비스5908
 
2.5%
아슈라프 하키미5639
 
2.4%
리스 제임스5535
 
2.4%
에우제비우5344
 
2.3%
잔루이지 돈나룸마2849
 
1.2%
킬리안 음바페2577
 
1.1%
피카요 토모리2461
 
1.1%
Other values (6452)168321
72.5%
(Missing)2575
 
1.1%
2021-01-27T18:10:47.344218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
에르난데스14650
 
3.5%
테오14371
 
3.4%
굴리트10046
 
2.4%
루드10044
 
2.4%
에데르6533
 
1.6%
밀리탕6528
 
1.6%
제임스6036
 
1.4%
데이비스5985
 
1.4%
알폰소5910
 
1.4%
리스5657
 
1.3%
Other values (6902)335018
79.6%

Most occurring characters

ValueCountFrequency (%)
191223
 
12.8%
69342
 
4.6%
56206
 
3.8%
54616
 
3.7%
41379
 
2.8%
35153
 
2.4%
32661
 
2.2%
25607
 
1.7%
24621
 
1.6%
24162
 
1.6%
Other values (749)938297
62.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter1265264
84.7%
Space Separator191223
 
12.8%
Uppercase Letter15324
 
1.0%
Other Punctuation15232
 
1.0%
Dash Punctuation5364
 
0.4%
Lowercase Letter860
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
69342
 
5.5%
56206
 
4.4%
54616
 
4.3%
41379
 
3.3%
35153
 
2.8%
32661
 
2.6%
25607
 
2.0%
24621
 
1.9%
24162
 
1.9%
23516
 
1.9%
Other values (713)878001
69.4%
ValueCountFrequency (%)
T2142
14.0%
J2035
13.3%
A1920
12.5%
M1238
 
8.1%
R1025
 
6.7%
C968
 
6.3%
P850
 
5.5%
S656
 
4.3%
F624
 
4.1%
G526
 
3.4%
Other values (22)3340
21.8%
ValueCountFrequency (%)
191223
100.0%
ValueCountFrequency (%)
-5364
100.0%
ValueCountFrequency (%)
r860
100.0%
ValueCountFrequency (%)
.15232
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1265264
84.7%
Common211819
 
14.2%
Latin16184
 
1.1%

Most frequent character per script

ValueCountFrequency (%)
69342
 
5.5%
56206
 
4.4%
54616
 
4.3%
41379
 
3.3%
35153
 
2.8%
32661
 
2.6%
25607
 
2.0%
24621
 
1.9%
24162
 
1.9%
23516
 
1.9%
Other values (713)878001
69.4%
ValueCountFrequency (%)
T2142
13.2%
J2035
12.6%
A1920
11.9%
M1238
 
7.6%
R1025
 
6.3%
C968
 
6.0%
r860
 
5.3%
P850
 
5.3%
S656
 
4.1%
F624
 
3.9%
Other values (23)3866
23.9%
ValueCountFrequency (%)
191223
90.3%
.15232
 
7.2%
-5364
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul1265264
84.7%
ASCII227986
 
15.3%
None17
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
69342
 
5.5%
56206
 
4.4%
54616
 
4.3%
41379
 
3.3%
35153
 
2.8%
32661
 
2.6%
25607
 
2.0%
24621
 
1.9%
24162
 
1.9%
23516
 
1.9%
Other values (713)878001
69.4%
ValueCountFrequency (%)
191223
83.9%
.15232
 
6.7%
-5364
 
2.4%
T2142
 
0.9%
J2035
 
0.9%
A1920
 
0.8%
M1238
 
0.5%
R1025
 
0.4%
C968
 
0.4%
r860
 
0.4%
Other values (19)5979
 
2.6%
ValueCountFrequency (%)
Ł3
17.6%
Ö3
17.6%
Á3
17.6%
İ3
17.6%
Ș2
11.8%
Ó2
11.8%
Ç1
 
5.9%

선수18
Categorical

HIGH CARDINALITY
MISSING

Distinct6773
Distinct (%)3.0%
Missing2679
Missing (%)1.2%
Memory size1.8 MiB
에우제비우
 
17225
에데르 밀리탕
 
8922
리스 제임스
 
7967
테오 에르난데스
 
6916
알폰소 데이비스
 
5892
Other values (6768)
182529 

Length

Max length15
Median length6
Mean length6.190537413
Min length1

Characters and Unicode

Total characters1420425
Distinct characters762
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2163 ?
Unique (%)0.9%

Sample

1st row김민재
2nd row주앙 펠릭스
3rd row테오 에르난데스
4th row리스 제임스
5th row김민재
ValueCountFrequency (%)
에우제비우17225
 
7.4%
에데르 밀리탕8922
 
3.8%
리스 제임스7967
 
3.4%
테오 에르난데스6916
 
3.0%
알폰소 데이비스5892
 
2.5%
루드 굴리트4812
 
2.1%
레길론4567
 
2.0%
아슈라프 하키미4128
 
1.8%
메이슨 그린우드2467
 
1.1%
브랜던 윌리엄스2277
 
1.0%
Other values (6763)164278
70.8%
(Missing)2679
 
1.2%
2021-01-27T18:10:47.584448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
에우제비우17225
 
4.3%
에데르8924
 
2.2%
밀리탕8922
 
2.2%
제임스8350
 
2.1%
리스8022
 
2.0%
에르난데스7106
 
1.8%
테오6922
 
1.7%
데이비스5937
 
1.5%
알폰소5895
 
1.5%
굴리트4830
 
1.2%
Other values (7175)318927
79.5%

Most occurring characters

ValueCountFrequency (%)
171609
 
12.1%
65939
 
4.6%
53750
 
3.8%
46159
 
3.2%
46068
 
3.2%
43796
 
3.1%
34143
 
2.4%
30153
 
2.1%
29473
 
2.1%
29228
 
2.1%
Other values (752)870107
61.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter1206592
84.9%
Space Separator171609
 
12.1%
Uppercase Letter18574
 
1.3%
Other Punctuation18530
 
1.3%
Dash Punctuation4289
 
0.3%
Lowercase Letter830
 
0.1%
Decimal Number1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
65939
 
5.5%
53750
 
4.5%
46159
 
3.8%
46068
 
3.8%
43796
 
3.6%
34143
 
2.8%
30153
 
2.5%
29473
 
2.4%
29228
 
2.4%
21816
 
1.8%
Other values (714)806067
66.8%
ValueCountFrequency (%)
J2454
13.2%
T2175
11.7%
A1751
 
9.4%
M1553
 
8.4%
C1175
 
6.3%
R1066
 
5.7%
E1029
 
5.5%
P954
 
5.1%
G926
 
5.0%
S847
 
4.6%
Other values (23)4644
25.0%
ValueCountFrequency (%)
171609
100.0%
ValueCountFrequency (%)
.18530
100.0%
ValueCountFrequency (%)
r830
100.0%
ValueCountFrequency (%)
-4289
100.0%
ValueCountFrequency (%)
41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul1206592
84.9%
Common194429
 
13.7%
Latin19404
 
1.4%

Most frequent character per script

ValueCountFrequency (%)
65939
 
5.5%
53750
 
4.5%
46159
 
3.8%
46068
 
3.8%
43796
 
3.6%
34143
 
2.8%
30153
 
2.5%
29473
 
2.4%
29228
 
2.4%
21816
 
1.8%
Other values (714)806067
66.8%
ValueCountFrequency (%)
J2454
12.6%
T2175
 
11.2%
A1751
 
9.0%
M1553
 
8.0%
C1175
 
6.1%
R1066
 
5.5%
E1029
 
5.3%
P954
 
4.9%
G926
 
4.8%
S847
 
4.4%
Other values (24)5474
28.2%
ValueCountFrequency (%)
171609
88.3%
.18530
 
9.5%
-4289
 
2.2%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul1206592
84.9%
ASCII213802
 
15.1%
None31
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
65939
 
5.5%
53750
 
4.5%
46159
 
3.8%
46068
 
3.8%
43796
 
3.6%
34143
 
2.8%
30153
 
2.5%
29473
 
2.4%
29228
 
2.4%
21816
 
1.8%
Other values (714)806067
66.8%
ValueCountFrequency (%)
171609
80.3%
.18530
 
8.7%
-4289
 
2.0%
J2454
 
1.1%
T2175
 
1.0%
A1751
 
0.8%
M1553
 
0.7%
C1175
 
0.5%
R1066
 
0.5%
E1029
 
0.5%
Other values (21)8171
 
3.8%
ValueCountFrequency (%)
Ł13
41.9%
Ö8
25.8%
Ș3
 
9.7%
Ó3
 
9.7%
Đ2
 
6.5%
Ž1
 
3.2%
İ1
 
3.2%

블락시도
Real number (ℝ≥0)

ZEROS

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.048072201
Minimum0
Maximum31
Zeros4290
Zeros (%)1.8%
Memory size1.8 MiB
2021-01-27T18:10:47.677704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median7
Q39
95-th percentile13
Maximum31
Range31
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.488858101
Coefficient of variation (CV)0.4950088481
Kurtosis0.5947490913
Mean7.048072201
Median Absolute Deviation (MAD)2
Skewness0.5588590437
Sum1636069
Variance12.17213085
MonotocityNot monotonic
2021-01-27T18:10:47.766970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
627934
12.0%
727107
11.7%
526191
11.3%
824130
10.4%
421496
9.3%
920006
8.6%
315767
6.8%
1015599
6.7%
1111506
 
5.0%
29221
 
4.0%
Other values (20)33173
14.3%
ValueCountFrequency (%)
04290
 
1.8%
14460
 
1.9%
29221
4.0%
315767
6.8%
421496
9.3%
ValueCountFrequency (%)
311
 
< 0.1%
291
 
< 0.1%
273
 
< 0.1%
268
< 0.1%
2518
< 0.1%

블락성공
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6618705036
Minimum0
Maximum7
Zeros119967
Zeros (%)51.7%
Memory size1.8 MiB
2021-01-27T18:10:47.850285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8142340694
Coefficient of variation (CV)1.230201474
Kurtosis1.459867492
Mean0.6618705036
Median Absolute Deviation (MAD)0
Skewness1.221839886
Sum153640
Variance0.6629771197
MonotocityNot monotonic
2021-01-27T18:10:47.922980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0119967
51.7%
178781
33.9%
226518
 
11.4%
35786
 
2.5%
4941
 
0.4%
5122
 
0.1%
614
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
0119967
51.7%
178781
33.9%
226518
 
11.4%
35786
 
2.5%
4941
 
0.4%
ValueCountFrequency (%)
71
 
< 0.1%
614
 
< 0.1%
5122
 
0.1%
4941
 
0.4%
35786
2.5%

태클시도
Real number (ℝ≥0)

ZEROS

Distinct57
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.62329729
Minimum0
Maximum57
Zeros3050
Zeros (%)1.3%
Memory size1.8 MiB
2021-01-27T18:10:48.019474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19
median13
Q318
95-th percentile25
Maximum57
Range57
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.634507964
Coefficient of variation (CV)0.4869972241
Kurtosis0.4638866383
Mean13.62329729
Median Absolute Deviation (MAD)4
Skewness0.5245519115
Sum3162376
Variance44.01669592
MonotocityNot monotonic
2021-01-27T18:10:48.122176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1214252
 
6.1%
1314104
 
6.1%
1113943
 
6.0%
1013635
 
5.9%
1413549
 
5.8%
1513015
 
5.6%
912665
 
5.5%
1612070
 
5.2%
811595
 
5.0%
1711117
 
4.8%
Other values (47)102185
44.0%
ValueCountFrequency (%)
03050
1.3%
11225
 
0.5%
22382
1.0%
33651
1.6%
45336
2.3%
ValueCountFrequency (%)
571
 
< 0.1%
562
< 0.1%
551
 
< 0.1%
533
< 0.1%
523
< 0.1%

태클성공
Real number (ℝ≥0)

ZEROS

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.041459527
Minimum0
Maximum29
Zeros4107
Zeros (%)1.8%
Memory size1.8 MiB
2021-01-27T18:10:48.222237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median7
Q39
95-th percentile13
Maximum29
Range29
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.440828466
Coefficient of variation (CV)0.4886527364
Kurtosis0.3834869153
Mean7.041459527
Median Absolute Deviation (MAD)2
Skewness0.4825178028
Sum1634534
Variance11.83930053
MonotocityNot monotonic
2021-01-27T18:10:48.311518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
627392
11.8%
726853
11.6%
525812
11.1%
824615
10.6%
421484
9.3%
920419
8.8%
1016170
7.0%
315568
6.7%
1111763
 
5.1%
29434
 
4.1%
Other values (19)32620
14.1%
ValueCountFrequency (%)
04107
 
1.8%
14622
 
2.0%
29434
4.1%
315568
6.7%
421484
9.3%
ValueCountFrequency (%)
291
 
< 0.1%
274
 
< 0.1%
263
 
< 0.1%
257
< 0.1%
2412
< 0.1%

패스시도
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct254
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.0269676
Minimum0
Maximum289
Zeros2487
Zeros (%)1.1%
Memory size1.8 MiB
2021-01-27T18:10:48.416896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile63
Q192
median107
Q3123
95-th percentile153
Maximum289
Range289
Interquartile range (IQR)31

Descriptive statistics

Standard deviation28.96209098
Coefficient of variation (CV)0.2706055457
Kurtosis2.379015422
Mean107.0269676
Median Absolute Deviation (MAD)15
Skewness-0.4007904431
Sum24844170
Variance838.8027139
MonotocityNot monotonic
2021-01-27T18:10:48.524111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1064222
 
1.8%
1074162
 
1.8%
1084160
 
1.8%
1054117
 
1.8%
1034113
 
1.8%
1044087
 
1.8%
1104012
 
1.7%
1013980
 
1.7%
1023978
 
1.7%
1113967
 
1.7%
Other values (244)191332
82.4%
ValueCountFrequency (%)
02487
1.1%
155
 
< 0.1%
269
 
< 0.1%
339
 
< 0.1%
426
 
< 0.1%
ValueCountFrequency (%)
2891
< 0.1%
2711
< 0.1%
2701
< 0.1%
2621
< 0.1%
2611
< 0.1%

패스성공
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct236
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.19426614
Minimum0
Maximum273
Zeros2523
Zeros (%)1.1%
Memory size1.8 MiB
2021-01-27T18:10:48.635207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile55
Q181
median96
Q3112
95-th percentile140
Maximum273
Range273
Interquartile range (IQR)31

Descriptive statistics

Standard deviation27.22128792
Coefficient of variation (CV)0.2829824377
Kurtosis1.966105275
Mean96.19426614
Median Absolute Deviation (MAD)15
Skewness-0.2628442207
Sum22329575
Variance740.9985162
MonotocityNot monotonic
2021-01-27T18:10:48.738269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
974272
 
1.8%
914215
 
1.8%
924193
 
1.8%
954186
 
1.8%
934184
 
1.8%
964168
 
1.8%
994151
 
1.8%
904129
 
1.8%
944116
 
1.8%
1014066
 
1.8%
Other values (226)190450
82.0%
ValueCountFrequency (%)
02523
1.1%
164
 
< 0.1%
247
 
< 0.1%
334
 
< 0.1%
421
 
< 0.1%
ValueCountFrequency (%)
2731
< 0.1%
2581
< 0.1%
2531
< 0.1%
2472
< 0.1%
2361
< 0.1%

숏패스시도
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct221
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.8293327
Minimum0
Maximum262
Zeros2807
Zeros (%)1.2%
Memory size1.8 MiB
2021-01-27T18:10:48.844991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39
Q164
median79
Q395
95-th percentile123
Maximum262
Range262
Interquartile range (IQR)31

Descriptive statistics

Standard deviation26.21252319
Coefficient of variation (CV)0.3283570374
Kurtosis1.181702224
Mean79.8293327
Median Absolute Deviation (MAD)15
Skewness0.02037430735
Sum18530783
Variance687.0963717
MonotocityNot monotonic
2021-01-27T18:10:48.954416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
794185
 
1.8%
764125
 
1.8%
774121
 
1.8%
754086
 
1.8%
784065
 
1.8%
824056
 
1.7%
734044
 
1.7%
814019
 
1.7%
804017
 
1.7%
844006
 
1.7%
Other values (211)191406
82.5%
ValueCountFrequency (%)
02807
1.2%
193
 
< 0.1%
250
 
< 0.1%
344
 
< 0.1%
439
 
< 0.1%
ValueCountFrequency (%)
2621
< 0.1%
2471
< 0.1%
2441
< 0.1%
2381
< 0.1%
2291
< 0.1%

숏패스성공
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct212
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.25148408
Minimum0
Maximum255
Zeros2816
Zeros (%)1.2%
Memory size1.8 MiB
2021-01-27T18:10:49.060345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q160
median75
Q390
95-th percentile117
Maximum255
Range255
Interquartile range (IQR)30

Descriptive statistics

Standard deviation25.13192416
Coefficient of variation (CV)0.333972472
Kurtosis1.116478716
Mean75.25148408
Median Absolute Deviation (MAD)15
Skewness0.058844885
Sum17468127
Variance631.6136119
MonotocityNot monotonic
2021-01-27T18:10:49.176724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
714290
 
1.8%
764276
 
1.8%
734275
 
1.8%
754259
 
1.8%
694236
 
1.8%
724211
 
1.8%
704206
 
1.8%
744202
 
1.8%
784100
 
1.8%
774086
 
1.8%
Other values (202)189989
81.8%
ValueCountFrequency (%)
02816
1.2%
187
 
< 0.1%
254
 
< 0.1%
347
 
< 0.1%
453
 
< 0.1%
ValueCountFrequency (%)
2551
< 0.1%
2341
< 0.1%
2321
< 0.1%
2201
< 0.1%
2192
< 0.1%

롱패스시도
Real number (ℝ≥0)

ZEROS

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.985495197
Minimum0
Maximum68
Zeros11239
Zeros (%)4.8%
Memory size1.8 MiB
2021-01-27T18:10:49.282392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile11
Maximum68
Range68
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.265310575
Coefficient of variation (CV)0.6549621345
Kurtosis2.260769863
Mean4.985495197
Median Absolute Deviation (MAD)2
Skewness0.976957296
Sum1157283
Variance10.66225315
MonotocityNot monotonic
2021-01-27T18:10:49.379430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
430283
13.0%
330126
13.0%
527942
12.0%
225916
11.2%
623543
10.1%
718774
8.1%
118035
7.8%
814497
6.2%
011239
 
4.8%
910265
 
4.4%
Other values (27)21510
9.3%
ValueCountFrequency (%)
011239
 
4.8%
118035
7.8%
225916
11.2%
330126
13.0%
430283
13.0%
ValueCountFrequency (%)
681
 
< 0.1%
501
 
< 0.1%
431
 
< 0.1%
331
 
< 0.1%
324
< 0.1%

롱패스성공
Real number (ℝ≥0)

ZEROS

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.346943523
Minimum0
Maximum63
Zeros24078
Zeros (%)10.4%
Memory size1.8 MiB
2021-01-27T18:10:49.481629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile8
Maximum63
Range63
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5929889
Coefficient of variation (CV)0.7747333895
Kurtosis3.788001654
Mean3.346943523
Median Absolute Deviation (MAD)2
Skewness1.242996779
Sum776926
Variance6.723591436
MonotocityNot monotonic
2021-01-27T18:10:49.571875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
240921
17.6%
337182
16.0%
136544
15.7%
429906
12.9%
024078
10.4%
521932
9.4%
615083
 
6.5%
79944
 
4.3%
86479
 
2.8%
93996
 
1.7%
Other values (21)6065
 
2.6%
ValueCountFrequency (%)
024078
10.4%
136544
15.7%
240921
17.6%
337182
16.0%
429906
12.9%
ValueCountFrequency (%)
631
 
< 0.1%
481
 
< 0.1%
291
 
< 0.1%
281
 
< 0.1%
263
< 0.1%

쓰루패스시도
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct77
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.05487442
Minimum0
Maximum85
Zeros3072
Zeros (%)1.3%
Memory size1.8 MiB
2021-01-27T18:10:49.678410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q113
median17
Q323
95-th percentile32
Maximum85
Range85
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.02245088
Coefficient of variation (CV)0.4443371187
Kurtosis1.189862957
Mean18.05487442
Median Absolute Deviation (MAD)5
Skewness0.6521103767
Sum4191078
Variance64.35971813
MonotocityNot monotonic
2021-01-27T18:10:49.782613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1512850
 
5.5%
1612669
 
5.5%
1712593
 
5.4%
1412544
 
5.4%
1812037
 
5.2%
1311922
 
5.1%
1911503
 
5.0%
1210872
 
4.7%
2010843
 
4.7%
2110060
 
4.3%
Other values (67)114237
49.2%
ValueCountFrequency (%)
03072
1.3%
1366
 
0.2%
2637
 
0.3%
3878
 
0.4%
41352
0.6%
ValueCountFrequency (%)
852
< 0.1%
781
 
< 0.1%
751
 
< 0.1%
733
< 0.1%
722
< 0.1%

쓰루패스성공
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct68
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.9236807
Minimum0
Maximum74
Zeros3181
Zeros (%)1.4%
Memory size1.8 MiB
2021-01-27T18:10:49.890237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q110
median14
Q319
95-th percentile28
Maximum74
Range74
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.103807885
Coefficient of variation (CV)0.4760091046
Kurtosis1.245995507
Mean14.9236807
Median Absolute Deviation (MAD)4
Skewness0.7452798219
Sum3464234
Variance50.46408647
MonotocityNot monotonic
2021-01-27T18:10:49.997278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1214578
 
6.3%
1314441
 
6.2%
1114265
 
6.1%
1414011
 
6.0%
1513485
 
5.8%
1013101
 
5.6%
1612458
 
5.4%
911941
 
5.1%
1711461
 
4.9%
1810362
 
4.5%
Other values (58)102027
44.0%
ValueCountFrequency (%)
03181
1.4%
1601
 
0.3%
21133
 
0.5%
31807
0.8%
43029
1.3%
ValueCountFrequency (%)
741
< 0.1%
731
< 0.1%
672
< 0.1%
641
< 0.1%
631
< 0.1%

드리븐패스시도
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.029151768
Minimum0
Maximum36
Zeros46754
Zeros (%)20.1%
Memory size1.8 MiB
2021-01-27T18:10:50.102524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile9
Maximum36
Range36
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.897704618
Coefficient of variation (CV)0.9566059543
Kurtosis3.318404576
Mean3.029151768
Median Absolute Deviation (MAD)2
Skewness1.454406035
Sum703157
Variance8.396692055
MonotocityNot monotonic
2021-01-27T18:10:50.199992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
046754
20.1%
138470
16.6%
235651
15.4%
330129
13.0%
423564
10.2%
517581
 
7.6%
612843
 
5.5%
78852
 
3.8%
86241
 
2.7%
94019
 
1.7%
Other values (23)8026
 
3.5%
ValueCountFrequency (%)
046754
20.1%
138470
16.6%
235651
15.4%
330129
13.0%
423564
10.2%
ValueCountFrequency (%)
361
 
< 0.1%
314
< 0.1%
301
 
< 0.1%
294
< 0.1%
283
< 0.1%

드리븐패스성공
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.489768664
Minimum0
Maximum32
Zeros58402
Zeros (%)25.2%
Memory size1.8 MiB
2021-01-27T18:10:50.302785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile7
Maximum32
Range32
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.55527
Coefficient of variation (CV)1.026308202
Kurtosis3.763869118
Mean2.489768664
Median Absolute Deviation (MAD)2
Skewness1.554644732
Sum577950
Variance6.529404773
MonotocityNot monotonic
2021-01-27T18:10:50.395290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
058402
25.2%
143990
19.0%
237312
16.1%
328950
12.5%
421006
 
9.0%
514757
 
6.4%
69989
 
4.3%
76398
 
2.8%
84248
 
1.8%
92592
 
1.1%
Other values (21)4486
 
1.9%
ValueCountFrequency (%)
058402
25.2%
143990
19.0%
237312
16.1%
328950
12.5%
421006
 
9.0%
ValueCountFrequency (%)
321
 
< 0.1%
292
 
< 0.1%
281
 
< 0.1%
272
 
< 0.1%
266
< 0.1%

Interactions

2021-01-27T18:08:23.826007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:23.948564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:24.071077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:24.194395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:24.311896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:24.440042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:24.562202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:24.693219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:24.813613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:24.941244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:25.061002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:25.188438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:25.317352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:25.444761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:25.565420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:25.691728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:25.814567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:25.935871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:26.060570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:26.183785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:26.304815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:26.433003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:26.554954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:26.676524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:26.797001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:26.923687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:27.044762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:27.165692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:27.300143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:27.429716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:27.552122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:27.674658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:27.803328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:27.923613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:28.045239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:28.161051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:28.277611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:28.395519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:28.512634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:28.633942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:28.750555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:28.871963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:29.001976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:29.122128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:29.247991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:29.374019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:29.496772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:29.618828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:29.745670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:29.863629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:29.990711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:30.134374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:30.279059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:30.409927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:30.537308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:30.669154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:30.793254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:30.918498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:31.045565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:31.174011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:31.294590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:31.422161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:31.546257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:31.674202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:31.796847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:31.920511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:32.041654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:32.168501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:32.293678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:32.422528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:32.544086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:32.673427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:32.806714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:32.938172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:33.061536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:33.194634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:33.326829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:33.457158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:33.595846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:34.148990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:34.281399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:34.411926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:34.537938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:34.667567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:34.794804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:34.928442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:35.056846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:35.186135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:35.317036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:35.458947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:35.584982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:35.709953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:35.831337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:35.957202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:36.075664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:36.194812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:36.317974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:36.451424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:36.580341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:36.707537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:36.831644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:36.960074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:08:37.095725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-01-27T18:10:23.480270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:23.609426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:23.738877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:23.864902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:23.996210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:24.126801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:24.251976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:24.379013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:24.511619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:24.643672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:24.770792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:24.909612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:25.035399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:25.171991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:25.307093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:25.446805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:25.573285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:25.709290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:25.841337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:25.971933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:26.107074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:26.236256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:26.369157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:26.499558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:26.629024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:26.757150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:26.889295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:27.020403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:27.153945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:27.279696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-27T18:10:27.418677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-01-27T18:10:50.534708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-27T18:10:50.849850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-27T18:10:51.162459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-27T18:10:51.483161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-01-27T18:10:51.753793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-01-27T18:10:28.586539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-01-27T18:10:32.112780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-01-27T18:10:34.936648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-01-27T18:10:36.145917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

matchID득점수전체슈팅유효슈팅자살골헤딩슛헤딩골프리킥프리킥골중거리슛중거리골패널티킥패널티골날짜유저이름경기결과파울부상레드카드옐로카드드리블횟수코너킥횟수점유율오프사이드횟수평점선수1선수2선수3선수4선수5선수6선수7선수8선수9선수10선수11선수12선수13선수14선수15선수16선수17선수18블락시도블락성공태클시도태클성공패스시도패스성공숏패스시도숏패스성공롱패스시도롱패스성공쓰루패스시도쓰루패스성공드리븐패스시도드리븐패스성공
015523030310100010002021-01-23T04:00:32TeamKoreaNo101008425113.63889김두현홍명보조원희유상철M. 페르난데스페테르 굴라치정산김신욱홍철손흥민신세계알레시오 크라뇨고광민권용현알렉스폴 베르나르도니에밀 아우데로김민재8050125114949076171365
115523040320000030002021-01-23T03:49:12SaddlerHS01008705103.52222에마뉘엘 프티파벨 네드베드크리스티아누 호날두티아구 실바카일 워커카밀 글리크티보 쿠르투아마티아스 치머만손흥민토마시 쿠베크마우로 이카르디레뱅 퀴르자와라파엘 바란루드 굴리트폴 베르나르도니테오 에르난데스이르지 파블렌카주앙 펠릭스81179114103817388211933
215523050310200000002021-01-23T03:38:04워리어00007514004.31667미하엘 발락파벨 네드베드스티븐 제라드크리스티아누 호날두호나우두Z. 이브라히모비치페페카림 벤제마아르투로 비달제롬 보아텡차범근티보 쿠르투아손흥민프랑크 레이카르트루드 굴리트미카엘 라우드루프페를랑 멘디테오 에르난데스1412910948371641297743
315523061960101040002021-01-23T03:25:26울산이창욱000011934905.22778파벨 네드베드에마뉘엘 프티미하엘 발락야프 스탐호나우두Z. 이브라히모비치가레스 베일이반 페리시치패트릭 반안홀트파울리뉴루카시 흐라데키조엘 마티프토마시 홀리버질 반데이크세르주 그나브리루드 굴리트로드리리스 제임스1002681641471411313110897
415523070200000000002021-01-23T03:07:50TeamKoreaNo100008504913.76667김두현홍명보조원희유상철페테르 굴라치정산김신욱홍철손흥민신세계고광민케빈 호드리게스권용현페드루 엔히키A. 사나브리아폴 베르나르도니에밀 아우데로김민재13074120107918910191296
515523080100000000002021-01-23T02:50:15CrazyWin곽준혁000011005004.00000프랭크 램파드호나우지뉴토니 크로스제롬 보아텡카일 워커조르디 알바네이마르 Jr.C. 판틸리몬케빈 폴란트해리 케인버질 반데이크닉 포프세르주 그나브리마테오 코바치치루드 굴리트오마르 에다리주니어 피르포에우제비우82281715213512811655161321
615523092650001042002021-01-20T05:02:08asdfasdfvv00007404803.99444패트릭 반안홀트세르지오 부스케츠요한 크루이프버질 반데이크S. 댈러스퀸시 프로머스프랑크 레이카르트루드 굴리트클레망 랑글레티모시 포수-멘사임민혁S. 아드리안M. 오베르마르스야프 스탐에드윈 반데르사르로날트 쿠만케빈-프린스 보아텡네벤 수보티치93531291141111024314900
715523101310000010002021-01-20T04:40:29읍매000011406003.93889미하엘 발락콰레스마크리스티아누 호날두데쿠페페브루누 알베스조르조 키엘리니나니주앙 칸셀루피카요 토모리송시우후벵 비나그르에우제비우카를로 핀솔리오티보 쿠르투아케빈 더브라위너루드 굴리트P. 베루아토711061751641531466612933
815523110220000010002021-01-20T04:29:21Chajung00008215303.87778에데르 밀리탕에우제비우미하엘 발락스티븐 제라드크리스티아누 호날두가레스 베일최철순마루안 펠라이니길레르미이반 페리시치제롬 보아텡티보 쿠르투아조바니 모레노라파엘 바란마리우 후이루크 쇼페를랑 멘디Z. 던리308611096857655171322
915523120220000020002021-01-20T04:08:24Uncover오센세00007604903.25000케빈 더브라위너라파엘 바란버질 반데이크세르주 그나브리루드 굴리트김영빈잔루이지 돈나룸마리스 제임스에우제비우에마뉘엘 프티미하엘 발락에드윈 반데르사르페페호세 크레스포제롬 보아텡패트릭 반안홀트네이마르 Jr.티보 쿠르투아50521191049990428765

Last rows

matchID득점수전체슈팅유효슈팅자살골헤딩슛헤딩골프리킥프리킥골중거리슛중거리골패널티킥패널티골날짜유저이름경기결과파울부상레드카드옐로카드드리블횟수코너킥횟수점유율오프사이드횟수평점선수1선수2선수3선수4선수5선수6선수7선수8선수9선수10선수11선수12선수13선수14선수15선수16선수17선수18블락시도블락성공태클시도태클성공패스시도패스성공숏패스시도숏패스성공롱패스시도롱패스성공쓰루패스시도쓰루패스성공드리븐패스시도드리븐패스성공
23212017844232770000020002021-01-21T01:13:12도루왕나지완100010734804.24444야프 스탐리오 퍼디난드크리스티아누 호날두박지성웨인 루니나니세르히오 로메로파비우로멜루 루카쿠데헤아폴 포그바존 이건루크 쇼루드 굴리트로랑 블랑마커스 래시포드티모시 포수-멘사메이슨 그린우드20331617014511911196332276
23212117844245960101021002021-01-21T00:45:53도루왕나지완200012815214.28889야프 스탐리오 퍼디난드크리스티아누 호날두박지성웨인 루니나니파비우로멜루 루카쿠데헤아폴 포그바존 이건루크 쇼루드 굴리트로랑 블랑마커스 래시포드티모시 포수-멘사메이슨 그린우드세르히오 로메로9020916414711110518142519109
23212217844253940000010002021-01-21T00:24:58도루왕나지완01008415504.59444웨인 루니나니세르히오 로메로파비우로멜루 루카쿠데헤아폴 포그바루크 쇼루드 굴리트로랑 블랑마커스 래시포드티모시 포수-멘사메이슨 그린우드야프 스탐리오 퍼디난드크리스티아누 호날두박지성존 이건511161169666611072621147
23212317844260540000010002021-01-19T20:27:01체육교사01008414703.62222프랭크 램파드에르난 크레스포애슐리 콜아스미르 베고비치이반 페리시치패트릭 반안홀트페드로네이마르 Jr.티보 쿠르투아라파엘 바란압둘 라만 바바루드 굴리트에두아르 멘디빌리 길모어김호수마르셀 드사이에마뉘엘 프티미하엘 발락1318410999817866191411
23212417844274660000000002021-01-11T01:57:45도루왕나지완00007815304.73889크리스티아누 호날두박지성웨인 루니나니세르히오 로메로앙헬 디마리아파비우하파엘로멜루 루카쿠데헤아폴 포그바마르코스 로호루드 굴리트로랑 블랑마커스 래시포드티모시 포수-멘사라이언 긱스리오 퍼디난드5017149685605877221753
23212517844282850200010112021-01-11T01:48:52동래정씨00007045004.32778요안 펠레안드리 퍄토프안토니오 칸드레바이반 페리시치패트릭 반안홀트카일 워커네이마르 Jr.미하엘 발락스티븐 제라드크리스티아누 호날두오카자키 신지이스마일리라파엘 바란버질 반데이크조지 새빌루드 굴리트잔루이지 돈나룸마엄원상10174948356521513201711
23212617844290860100030002021-01-11T01:38:00도루왕나지완10007324703.62778라이언 긱스리오 퍼디난드크리스티아누 호날두박지성웨인 루니나니세르히오 로메로앙헬 디마리아파비우하파엘로멜루 루카쿠데헤아폴 포그바마르코스 로호루드 굴리트로랑 블랑마커스 래시포드티모시 포수-멘사8017810489767095151133
23212717844301860001020002021-01-11T01:15:07Sprezzatura9201009155103.57778루드 굴리트김진혁필 포든에데르 밀리탕데니스 베르캄프파벨 네드베드세스크 파브레가스로날트 쿠만패트릭 반안홀트카일 워커A. 드라고비치네이마르 Jr.루카시 흐라데키라파엘 바란버질 반데이크세르주 그나브리요나탄 타프랑크 레이카르트901151241149792111391212
23212817844310530100010002021-01-11T01:03:43se7en수제핫도그01008025304.10000미하엘 발락솔다도마누엘 노이어이반 페리시치제롬 보아텡호세 카예혼R. 레반도프스키토마스 뮐러데이비드 알라바마르코스 로호세르주 그나브리사울레온 고레츠카니클라스 쥘레요주아 키미히코랑탕 톨리소니콜라 페페알폰소 데이비스6122139581595631292400
23212917844321430000020002021-01-11T00:53:50쭈니홍00008915403.79444티보 쿠르투아A. 린데프랑크 레이카르트루드 굴리트치찬 스탄코비치파페 아부 시세W. 파리녜스테오 에르난데스무사 제네포리스 제임스파올로 말디니마르셀 드사이미하엘 발락에르난 크레스포호나우두로랑 코시엘니다비드 루이스몬토로90539185706854131033